Skip to main content Accessibility help
×
Hostname: page-component-848d4c4894-p2v8j Total loading time: 0.001 Render date: 2024-06-01T01:13:40.146Z Has data issue: false hasContentIssue false

Part II - Digital Media in the Adolescent Developmental Context

Published online by Cambridge University Press:  30 June 2022

Jacqueline Nesi
Affiliation:
Brown University, Rhode Island
Eva H. Telzer
Affiliation:
University of North Carolina, Chapel Hill
Mitchell J. Prinstein
Affiliation:
University of North Carolina, Chapel Hill

Summary

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2022
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This content is Open Access and distributed under the terms of the Creative Commons Attribution licence CC-BY-NC-ND 4.0 https://creativecommons.org/cclicenses/

3 Digital Media and the Dual Aspect of Adolescent Identity Development The Effects of Digital Media Use on Adolescents’ Commitments and Self-Stories

Hiromitsu Morita , Nastasia Griffioen , and Isabela Granic

Concerns abound about the impact of social media on adolescents as it increasingly becomes an integral part of their social lives. One of the concerns that has received a great deal of attention is the impact of social media on adolescents’ mental health (Gordon, Reference Gordon2020). A large number of studies have been conducted to investigate the impact; however, the findings are mixed, showing both positive and negative impact (Baker & Algorta, Reference Baker and Algorta2016; Best et al., Reference Best, Manktelow and Taylor2014; Seabrook et al., Reference Seabrook, Kern and Rickard2016). What is clear in this growing body of research with seemingly contradictory findings is that the relation between social media use and adolescent mental health is much more complex than originally thought. In line with the recognition of this complexity, more and more researchers examine the mechanism of this relation within the framework of a psychological theory (Keles et al., Reference Keles, McCrae and Grealish2019).

Adolescent Identity Development on Social Media

In a recent theoretical review, Granic et al. (Reference Granic, Morita and Scholten2020) suggested that, in order to understand the impact of digital media on adolescents’ mental health, it is essential to consider their core developmental concern: identity development. For decades, developmental psychologists have studied the challenging transition adolescents are expected to make in order to become functional members of society – that is, moving past identifying with the roles and values of others and toward making social commitments that are in accord with their own interests, aptitudes, and values (Erikson, Reference Erikson1968; Kroger, Reference Kroger2004). Whether or not adolescents successfully make this transition has important implications for their mental health (e.g., Azmitia et al., Reference Azmitia, Syed and Radmacher2013; Kuiper et al., Reference Kuiper, Kirsh and Maiolino2016). Since a considerable proportion of identity development processes is now taking place on social media, it is important to examine how the use of social media affects these processes.

A Model of Adolescent Identity Development

To present a model of adolescent identity development, we build on the theoretical framework proposed by Granic et al. (Reference Granic, Morita and Scholten2020). In this framework, progression toward (a) commitment to person–society integrated values and (b) the construction of a coherent life story constitutes adolescent identity development. The framework specifies key factors at intrapersonal, interpersonal, and cultural levels that shape adolescent identity development. Key factors at the intrapersonal level are psychological needs that drive adolescents to uphold and unite personal and social values and form a coherent life story. Key interpersonal factors are the characteristics of narrative partners that affect how adolescents construct and develop stories about themselves. Finally, key cultural factors are cultural values, norms, and narratives that set the boundaries within which adolescents explore and make commitment choices. This chapter focuses on processes at the interpersonal level, where intrapersonal and cultural factors intersect, as these processes are most pertinent to social media. Specifically, we discuss narrative and dialogical processes as an interpersonal mechanism of identity development (Hammack, Reference Hammack2008; McLean & Pasupathi, Reference McLean and Pasupathi2012). Through sharing self-stories with others, individuals encounter various perspectives, reflect on and learn about themselves, and consolidate or change their commitments, values, and narratives. Furthermore, we clearly differentiate between the subjective and objective aspects of identity by drawing on McAdams’s (Reference McAdams, Westenberg, Blasi and Cohn1998) exposition of the self-as-subject (meaning-making process) and the self-as-object (product of the meaning-making process). We explain how the two aspects of identity develop together through narrative and dialogical processes (see Figure 3.1).

Figure 3.1 The dual aspect of adolescent identity development and narrative and dialogical processes

Chapter Overview

We begin by describing the subjective aspect of identity development: changes in commitments and values.1 We explain how conventional commitments change to self-evaluated commitments during adolescence and the key role of introspection in this transition. We then describe the objective aspect of identity development: changes in a self-story, or narrative identity. We explain the process of constructing a coherent life story during adolescence and the function of narrative partners in this process. After the description of each aspect of adolescent identity development, we discuss how the use of social media may facilitate or hinder the key processes involved. Since the field of identity development is just beginning to incorporate social media in its research, our discussion will consist mainly of hypothetical links between social media use and adolescent identity development. However, the paucity of research in this area also means there are many avenues for future research. Therefore, the chapter concludes with future directions for studying the impact of social media use on adolescent identity development.

The Subjective Aspect of Identity Development: Changes in Commitments and Values

Identity is first and foremost the self as subject. Although developmental psychologists have taken different approaches to conceptualizing and studying the subjective self and its changes, there are some commonalities in their descriptions (Kroger, Reference Kroger2004). In this chapter we focus on Erikson’s (Reference Erikson1968) theory of the life cycle and Loevinger’s (Reference Loevinger1976) theory of ego development. Erikson laid out a series of crises that people face in their lifetime and must resolve for proper functioning and development. Although he described all the crises as having some bearing on identity, he characterized the fifth one – which takes place in adolescence – as a major crisis for identity. Erikson conceptualized identity in several different ways. However, later psychologists have focused on his conception of identity as ideological and occupational commitments and expanded it to include interpersonal commitments (e.g., Luyckx et al., Reference Luyckx, Goossens and Soenens2006; Marcia, Reference Marcia1966).

Loevinger (Reference Loevinger1976) also developed a theory of changes in the subjective self, namely ego development theory. Unlike Erikson’s theory, Loevinger’s theory is not built around chronological age, and therefore the ego stages are not tied to age-related challenges and tasks. The theory describes changes in various aspects of the self, such as impulse control, conscious preoccupations, and cognitive and interpersonal styles. At its core, ego development theory is about changes in an individual’s frame of reference, the values in accordance with which an individual makes experience meaningful and coherent (Hy & Loevinger, Reference Hy and Loevinger1996). In short, changes in commitments and values constitute the subjective aspect of identity development.

The Formation of Self-Evaluated Commitments in Adolescence

Identity development is a lifelong process. Throughout life, identity undergoes qualitative changes (Kroger, Reference Kroger2004). However, particular attention has been paid to the type of identity that is thought to mark the entrance to adulthood. According to Erikson’s (Reference Erikson1968) theory of the life cycle, childhood is a period in which individuals learn the roles of adults around them and focus on becoming skillful at preparatory tasks provided by their family, school, and community. Children are therefore identified with the roles and values of others in the immediate environment. In adolescence, psychological needs and social demands drive individuals to explore different occupations and ideologies in the larger society and commit to occupations and ideologies that match their own interests, aptitudes, and values to find their niche in society. This serves as the foundation for adulthood.

It is now widely recognized that identity exploration and commitment are iterative processes (Bosma & Kunnen, Reference Bosma and Kunnen2001; Grotevant, Reference Grotevant1987; Kerpelman et al., Reference Kerpelman, Pittman and Lamke1997; Luyckx et al., Reference Luyckx, Goossens and Soenens2006). For example, Luyckx et al. (Reference Luyckx, Goossens and Soenens2006, Reference Luyckx, Klimstra, Duriez, Van Petegem and Beyers2013) suggested that identity formation involves two cycles. The first one consists of exploration in breadth and commitment-making. In this cycle, individuals explore various values and goals and make initial commitments. The second cycle consists of exploration in depth and identification with commitment. Specifically, current commitments are continually re-evaluated through self-reflection and interpersonal dialogue, and if individuals feel confident about their commitments, they identify with them.

A similar developmental sequence can be found in Loevinger’s (Reference Loevinger1976) ego development theory: progression from the Conformist stage to the Conscientious stage via the Self-Aware stage. At the Conformist stage, social belonging is of paramount importance, and most effort is put into gaining acceptance by a social group. Individuals at this stage conform to the norms and values of their social groups, which are based on external characteristics (e.g., physical appearance, outward behavior). Thus, they seek social acceptance and recognition by trying to look or behave in a socially desirable manner. At the next, Self-Aware stage, individuals begin to explore inner aspects of themselves, and conformity starts to become less rigid. When the next, Conscientious stage is reached, individuals have gained a rich understanding of their motives and personality traits. Individuals at this stage therefore evaluate and commit to social values based on their internal characteristics (i.e., the formation of self-evaluated commitments; Loevinger, Reference Loevinger1987). Although ego development was conceptualized independently of chronological age, research has shown that the progression from the Conformist stage toward the Conscientious stage commonly takes place during adolescence (Syed & Seiffge-Krende, Reference Syed and Seiffge-Krenke2015; Westenberg & Gjerde, Reference Westenberg and Gjerde1999).

In sum, the transition from conventional commitments to self-evaluated commitments constitutes the subjective aspect of adolescent identity development. This transition is marked by changes in the mode of commitment – from conformity to self-evaluated commitment – and the nature of commitments – from external to internal characteristics.

Identity Exploration and Introspection

Exploration to gain an understanding of one’s environment and oneself is considered a key mechanism of identity development (Grotevant, Reference Grotevant1987). Erikson (Reference Erikson1968) emphasized the importance of psychosocial moratorium, the period during which adolescents explore different ideologies and occupations in society and find suitable ones. Such exploration entails introspection to find out one’s own interests, values, and aptitudes. In Loevinger’s (Reference Loevinger1976) ego development, we have seen that progression from the Conformist stage to the Conscientious stage goes through the Self-Aware stage, where individuals begin introspection to gain a deeper understanding of their internal characteristics. In order to move on from rigid conformity, individuals must shift their focus from external to internal aspects of themselves to understand their own interests and values, which they can then use to evaluate and choose social values to commit to. Indeed, introspection was found to be the most common factor in identity development (Kroger & Green, Reference Kroger and Green1996).

The capacity for introspection begins to develop in adolescence (Sebastian et al., Reference Sebastian, Burnett and Blakemore2008), making it a sensitive period for cultivating the capacity. Counseling, psychotherapy, and educational programs have been used to aid adolescents’ identity exploration (Kroger, Reference Kroger2004). Marcia (Reference Marcia1989) suggested that it is important to create an open and safe environment that encourages free exploration and serves as a safety net if adolescents’ choices go awry. Indeed, it has long been noted that open and accepting relationships are crucial in facilitating self-exploration (Rogers, Reference Rogers1961). As we discuss later in the chapter, identity exploration and introspection often happen during or following interpersonal dialogue, and the characteristics of conversational partners greatly affect the extent to which individuals engage in self-exploration and gain insights into themselves.

Social Media and the Adolescent Development of Commitments and Values

We have explained the adolescent development of commitments and values: the transition from conventional to self-evaluated commitments. We now discuss how the use of social media may affect this transition. To reiterate, identity exploration and introspection inherent in the exploration are a key mechanism through which adolescents move on from conformity and preoccupation with external characteristics and form self-evaluated commitments. Therefore, the use of social media would facilitate the transition if it supports identity exploration and introspection. Conversely, the use of social media would hinder the transition if it prevents identity exploration and introspection, increases conformity, and makes adolescents fixated on appearance.

A Playground for Identity Exploration

Social media provides the opportunity for people to try out different versions of themselves and see what feels right (Casserly, Reference Casserly2011). When asked about who they are on social media, people often report that they have different personas depending on the platform (Zhong et al., Reference Zhong, Chang, Karamshuk, Lee and Sastry2017). This may be because different platforms tend to attract different audiences by virtue of their design and functionality. For example, Instagram may be well suited to expressing people’s artistic side and therefore popular among artists, while Reddit may cater to their contemplative, intellectual side and attract curious minds and experts. The beauty of social media is therefore that all platforms taken together serve as a playground in which individuals can explore different aspects of themselves. However, there is also the downside of a plethora of options: Too many options can create paralysis and lead to ruminative exploration, keeping adolescents from completing the cycle of identity formation (Beyers & Luyckx, Reference Beyers and Luyckx2016). As we have discussed, successful adolescent identity development requires not only exploration of commitment options but also introspection to evaluate whether these options fit one’s personality. Therefore, social media platforms that provide space for identity exploration as well as self-reflection may be more conducive to identity development than those that provide space for the former only.

Potent Social Norms and Values

While social media offers plenty of opportunities for identity exploration, the large scale of social media also enables potent social norms and values, potentially making it more difficult for adolescents to move on from conformity. Most adolescents in the pre–social media era are likely to have negotiated with trends and conventions that manifested themselves in a relatively small social group, at most on a national scale. With social media, however, adolescents now have the possibility to observe trends on a much larger global scale, likely experiencing greater pressure to conform to these trends. Indeed, norms and values on social media may have stronger influence than those offline because they are more widely shared and more readily accessed (Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018).

Social attitudes that currently prevail in Western cultures and may be magnified by social media are anti-mainstream sentiments (Vinh, Reference Vinh2021). Those who follow counterculture movements are usually called hippies or hipsters, going against what – in their eyes – everyone else is doing. One reflection of this trend is the popularity of prank videos on social media, in which people violate social conventions and norms for entertainment. The anti-mainstream sentiments have appealed to so many people that they themselves have become the norm and the source of conformity. Thus, social media can spread and magnify trends rapidly to create potent social norms and values, including those that espouse anti-mainstream sentiments.

Inescapable Past Selves

An essential condition for identity development is the freedom to leave behind old identities and explore new ones. Unfortunately, such freedom is not always guaranteed on the Internet. What people say and do on the Internet is permanently recorded and often remains on the Internet for future generations to unearth (Eichhorn, Reference Eichhorn2019; Nesi et al., Reference Nesi, Telzer and Prinstein2020). This permanence of information on the Internet, especially on social media, can be problematic for identity development (see Davis & Weinstein, Reference Davis, Weinstein and Wright2017). Traces of old identities on social media can mislead others into thinking that the old identities still hold true and make it difficult for individuals to change or to fully embrace the change. There is the increasing occurrence of people being criticized or getting fired for what they said or did on the Internet in the past even though these views or deeds no longer reflect them (e.g., Arora, Reference Arora2021). Accordingly, there are signs of adolescents and young adults erasing their social media posts for fear of repercussions (Davis & Weinstein, Reference Davis, Weinstein and Wright2017; Jargon, Reference Jargon2020; Smith, Reference Smith2013). These privacy issues therefore seem to be making it difficult for individuals to free themselves from the past and move forward.

However, reminders of one’s past selves may also benefit identity development. In adolescent years, individuals may go through many different phases. For example, an adolescent may experience the “goth” phase from age 15 to 16 years, reflected in a series of photos of a black-clothed self with plaid skirts and chains. From age 17 to 18 years, this adolescent may be absorbed in environmentalism, reflected in posts and photos depicting community efforts and environmental protests. Transition between such phases may sometimes feel fluent and smooth and may be easily forgotten. Snippets of social media content and interactions in the past can help individuals recall parts of their past selves that they may otherwise have forgotten. Being reminded of past selves can also help individuals reflect on the development they have gone through and understand that identity is never permanent and continues to change over time (Pasupathi et al., Reference Pasupathi, Mansour and Brubaker2007). It is nonetheless important that the right to social media data belong to users so that they can access their past data when they want to and they can erase them if they deem them harmful to their current identity.

A Tool for Distraction or Introspection?

One of the concerns that social media has generated is that it may act as a distraction from oneself (The School of Life, n.d.). Social media is filled with information about people’s lives and world events, and passive usage of social media (e.g., reading news feeds) is more common than active usage (e.g., posting status updates; Verduyn et al., Reference Verduyn, Lee and Park2015). The implication is that people are focused mainly on other people’s lives on social media, leaving little room to reflect on their own lives and gain insights into themselves. Furthermore, information overload on social media, which has been shown to lead to “social media fatigue” in some (Bright et al., Reference Bright, Kleiser and Grau2015; Dhir et al., Reference Dhir, Kaur, Chen and Pallesen2019), may deplete cognitive resources necessary for digesting information and integrating it into a sense of self. Indeed, Misra and Stokols (Reference Misra and Stokols2012) found that individuals who experienced an overload of digital information spent less time on contemplative activities such as self-reflection.

Nevertheless, social media has some functionalities that could help people gain insights into themselves. For example, Facebook’s “Year in Review” posts provide users with a chance to reflect on their life experiences in the past year. Such reflection may bring people insights into what kind of things they value and are interested in and what they are good at. Furthermore, as we discuss later, social media significantly increases the chance to receive feedback about oneself from others (e.g., boyd & Heer, Reference boyd and Heer2006), thereby deepening self-understanding.

Emphasis on Appearance

Another important issue to consider is the extent to which social media promotes preoccupation with appearance. Although social media can be used for a variety of purposes, posting pictures of one’s physical appearance (i.e., selfies) is popular among adolescents, and many adolescents are preoccupied with how others perceive their physical appearance on social media (Boursier & Manna, Reference Boursier and Manna2018; Choukas-Bradley et al., Reference Choukas-Bradley, Nesi, Widman and Galla2020). The increasing use of clickbaits to attract followers on social media may also be contributing to their perceived importance of appearance and superficial impressions. Preoccupation with appearance on social media is associated with a number of negative mental health indices among adolescents (Choukas-Bradley et al., Reference Choukas-Bradley, Nesi, Widman and Galla2020). While having a healthy body image is important, adolescent identity development entails a shift in the source of self-esteem from external to internal attributes. Therefore, social media is likely to be harmful to adolescents to the extent that it makes them fixated on appearance.

The design and affordances of social media platforms may affect the degree to which adolescents focus on external aspects of themselves. For example, photo-based social media platforms such as Instagram may attract those who are concerned with physical appearance, and consequently social values and norms that revolve around physical appearance may be more prevalent on these platforms. Therefore, the use of photo-based platforms may put adolescents at higher risk of being influenced by appearance-based values and norms. Furthermore, given that individuals can explore and express their internal characteristics more easily using concepts and words rather than images, text-based platforms such as Reddit and Tumblr might be more conducive to introspection and the expression of inner qualities. It is nonetheless important to note that the nonverbal expression of internal characteristics is possible (e.g., an expression of creativity in dancing), and photo-based platforms can also serve as a place for more mature expressions of identity.

The Objective Aspect of Identity Development: Changes in a Narrative Identity

Thus far, we have discussed the subjective aspect of identity development: changes in commitments and values. We now turn to the objective aspect, manifestations of the changes. Commitments and values manifest themselves in several ways. For example, people strive to fulfill their commitments and values; therefore, commitments and values are manifested in goal-striving (Maslow, Reference Maslow1970; Schwartz, Reference Schwartz, Seligman, Olson and Zanna1996). Moreover, values act as a frame of reference in perception to create “coherent meanings in experience” (Hy & Loevinger, Reference Hy and Loevinger1996, p. 4). Thus, they are reflected in the meanings that individuals assign to objects and events. When this meaning-making process is applied to past experiences, it often takes the form of storytelling. A story about the self that an individual creates based on their past experiences has been termed “narrative identity” (McAdams, Reference McAdams2018; Singer, Reference Singer2004). In storytelling, individuals make sense of and organize their past experiences by relating them to important aspects of themselves (Pasupathi et al., Reference Pasupathi, Mansour and Brubaker2007). In other words, past experiences that are relevant to one’s goals and values (i.e., self-defining memories) make up the main contents of a narrative identity (Blagov & Singer, Reference Blagov and Singer2004; Singer et al., Reference Singer, Blagov, Berry and Oost2013). In short, commitments and values (identity-as-subject) act as the guiding principles of storytelling to make sense of and organize past experiences into a narrative identity (identity-as-object; see Figure 3.1).

The Construction of a Coherent Life Story in Adolescence

As adolescents’ commitments change from conventional to self-evaluated commitments, corresponding changes likely occur in their narrative identities. During adolescence, a narrative identity changes from relatively disjointed descriptions of roles and habits to an autobiographical narrative, which demonstrates more complex reflective thinking and a causal understanding of one’s life experiences (Habermas & de Silveira, Reference Habermas and de Silveira2008; McAdams, Reference McAdams, Westenberg, Blasi and Cohn1998). Specifically, adolescents’ narrative identities increasingly take the form of a life story, which tells how the past self has grown into the present self, which may then become an envisioned future self (McAdams, Reference McAdams2018; McAdams & McLean, Reference McAdams and McLean2013). Their past, present, and future become clearly differentiated and yet causally connected to form a temporally coherent life story (Habermas & Bluck, Reference Habermas and Bluck2000; Pasupathi et al., Reference Pasupathi, Mansour and Brubaker2007). In addition to temporal coherence, there is another type of coherence that likely emerges in adolescents’ narrative identities: person–society coherence (Syed & McLean, Reference Syed and McLean2016). This type of coherence shows alignment between individuals’ personal attributes and their social contexts. As discussed earlier, adolescent identity development is progression toward commitment to social values that match personal interests, values, and talents. Therefore, narrative identities likely exhibit temporal and person–society coherence toward the end of adolescent development, weaving together the past self that was identified with the roles and values of others, the present self that commits to person–society integrated values, and the future self that will fulfill these values.

Besides these structural changes, adolescent identity development is likely to be accompanied by related changes in the theme of a narrative identity. Granic et al. (Reference Granic, Morita and Scholten2020) suggested that the dynamics of the needs for agency and communion shift during adolescence and that this shift may be reflected in the relative prominence on agency versus communion themes in a narrative identity. Specifically, as adolescents start to engage in self-exploration and gain better insight into their own interests and values, a predominance of communion themes may give way to a predominance of agency themes (see Van Doeselaar et al., Reference Van Doeselaar, McLean, Meeus, Denissen and Klimstra2020, for some indirect support for this). Toward the end of adolescent identity development, when the needs for agency and communion become more balanced, agency and communion themes may become relatively equalized and united in a narrative identity.

Another theme that is relevant to adolescent identity development is external versus internal focus. As discussed earlier, the nature of commitments changes from external to internal characteristics during adolescence. Therefore, the theme of adolescents’ narrative identities is likely to change from that of trying to look good and behave properly to that of cultivating their inner traits.

Dialogue and the Function of Narrative Partners

Storytelling is inherently a social activity and therefore usually involves dialogue (Hammack, Reference Hammack2008; Hermans, Reference Hermans2004). A narrative identity expressed in storytelling can be affirmed or challenged, which may in turn consolidate, weaken, or change commitments and values (McLean & Pasupathi, Reference McLean and Pasupathi2012; Thorne, Reference Thorne2000; see Figure 3.1). Thus, storytelling and dialogue are an important mechanism of identity development. Whether storytelling and dialogue contribute meaningfully to identity development depends heavily on the characteristics of their narrative partners (McLean et al., Reference McLean, Pasupathi and Pals2007; Pasupathi & Hoyt, Reference Pasupathi and Hoyt2009). There are three essential functions that narrative partners serve: (a) elaboration, (b) grappling, and (c) attention and validation (Granic et al., Reference Granic, Morita and Scholten2020).

First, narrative partners help people elaborate on their stories (Fivush et al., Reference Fivush, Haden and Reese2006; Pasupathi et al., Reference Pasupathi, Mansour and Brubaker2007). To construct a meaningful and coherent life story from past experiences, you must derive meaning from these experiences, and a simple recounting of past experiences is often insufficient (Blagov & Singer, Reference Blagov and Singer2004; Singer et al., Reference Singer, Blagov, Berry and Oost2013). Therefore, narrative partners’ requests for elaboration are essential. Blagov and Singer (Reference Blagov and Singer2004) specified four dimensions of self-defining memories, which have implications for elaboration requests. Specifically, elaboration requests are likely to be especially helpful if they use a time frame most conducive to meaning-making in a given situation (e.g., “Tell me exactly what happened in that moment”; “How did you change during your college years?”) and ask about affect, or more specifically, emotional valence and intensity (e.g., “How did the experience make you feel?”; “How much impact did the experience have on you?”), content, or the relevance to values and goals (e.g., “Why is the experience important to you?”; “How does the experience help you achieve your goals?”), and meaning, or learning and growth (e.g., “What did you learn from the experience?”; “How did the experience change you as a person?”).

The second important function of narrative partners is “grappling”, which is an act of supporting identity exploration in a dialogue while maintaining an attitude of open-mindedness and patience (Granic et al., Reference Granic, Morita and Scholten2020). Engaging in dialogue with others is essentially identity exploration because different people uphold different values and you are encouraged to take others’ perspectives in dialogue (Hermans, Reference Hermans2004). Your values and narratives may sometimes be challenged in the process, and listening to alternative views may bring new insights and weaken or change your values and commitments. However, such a challenge is likely most fruitful when it is done in an open and accepting relationship (Rogers, Reference Rogers1961). Moreover, it is important that narrative partners remain patient despite unexpected perspective changes and contradictions, which are due to occur during identity exploration (Granic et al., Reference Granic, Morita and Scholten2020).

Finally, narrative partners provide attention and affirmation. When narrative partners listen attentively and affirm your life story, your values and goal endeavors are reflected back to you and become consolidated (McLean & Pasupathi, Reference McLean and Pasupathi2012; Pasupathi & Rich, Reference Pasupathi and Rich2005). Those who show interest and give you affirmation are usually the ones who share your values and commitments. As discussed earlier, adolescent identity development is considered complete when they find their niche in society, where like-minded others share the interests and values that adolescents have discovered through self-exploration (Erikson, Reference Erikson1968). Therefore, finding narrative partners who share personal values and aspirations is important especially toward the end of adolescent identity development.

Social Media and the Adolescent Development of a Narrative Identity

We have discussed how commitments and a narrative identity develop together through storytelling and dialogue in adolescence. In this section we explore different ways in which social media can support or obstruct storytelling and dialogue, thereby facilitating or hindering the adolescent development of a narrative identity.

Dialogue with Diverse Groups of People

Social media emerged with the advent of the Web 2.0, a dramatic change of the Internet from a place for passive consumption to active participation, interaction, and collaboration (Peters, Reference Peters2020). Anyone who has access to the Internet can express and share their views and stories on social media, and there are usually others – sometimes hundreds and thousands of people – who validate or reject their views and stories. Storytelling and dialogue in such a large, interconnected social environment have never existed before. Social media has made it easy to have dialogue with people who come from different cultures and backgrounds, thus significantly increasing the chance of encountering different perspectives. Since taking different perspectives during social interaction is essential for identity development (Hermans, Reference Hermans2004; Kroger, Reference Kroger2004), social media can be a great tool for adolescents’ identity development. While there is some concern about the increasing frequency of conflicts resulting from the increased contact with diverse groups of people, conflicts can be meaningful experiences and contribute to identity development, especially if they are managed with understanding (Rogers, Reference Rogers1961). Therefore, the presence of moderators who are discerning but also empathetic would be valuable.

Censorship

There has been growing concern and controversy surrounding censorship on social media (Heins, Reference Heins2014). Social media platforms such as Twitter and YouTube recently came under fire after deleting posts or banning users and channels that express certain ideological views (e.g., BBC News, 2020; Zaru, Reference Zaru2021). Social media companies have long suggested that their platforms provide a space for the free exchange of views and ideas. However, it has become apparent that these platforms are not neutral public platforms, but rather, just like traditional media, they promote certain content and suppress others according to their interests and ideologies (Lewis, Reference Lewis2021). Although social media companies began to acknowledge such editorial actions, it remains largely unclear how they are curating content. As we have just discussed, dialogue with diverse groups of people plays an essential role in identity development. Therefore, if social media platforms exercise editorial power, it is important that they make their decisions transparent so that people can make informed decisions about which platforms to use for a meaningful dialogue.

Narrative Elaboration on Social Media

Social media platforms generally support the elaboration of narratives as they provide comment sections and encourage dialogue between users. However, the unique affordances of social media platforms may affect the extent to which users elaborate on their stories. For example, Twitter sets a strict character limit for posts and comments and may therefore hinder the elaboration of narratives and deep dialogue compared to platforms like Facebook, Reddit, and Tumblr. Indeed, in an in-depth interview with activists, Comunello et al. (Reference Comunello, Mulargia and Parisi2016) found that the activists perceived platform affordances as having a significant impact on their ability to express themselves, with one of them reporting that the possibility to write longer texts allowed him to better articulate his opinions. Misunderstandings between users may be more common on platforms that restrict the length of posts and comments because short posts and comments do not easily allow clarification of meaning. Such platforms might predispose users to insult each other instead of asking each other questions to elaborate on their self-stories.

Attention and Validation on Social Media

Social media offers unprecedented opportunities to be listened to and validated by others. Before social media, individuals whose values and beliefs deviated from the norm had difficulty finding someone who would listen to or affirm their views (e.g., Gray, Reference Gray2009). It is now much easier to find like-minded others because social media platforms such as Facebook and Reddit enable people to search for various communities. This is, for example, reflected in online activism by various groups of people (Bennett, Reference Bennett2014; Buell Hirsch, Reference Buell Hirsch2014; Sandoval-Almazan & Gil-Gracia, Reference Sandoval-Almazan and Gil-Garcia2014). However, the downside of such diverse and specific communities is that they can create echo chambers and shun interaction outside the communities (Singer, Reference Singer2020). An optimal social media environment may therefore be that which makes it easy to find like-minded people while encouraging communication and interaction between diverse groups.

Attention and validation are most effective if they come from close people (Carr et al., Reference Carr, Wohn and Hayes2016). Hayes et al. (Reference Hayes, Carr and Wohn2016) found that people experienced more personal support on platforms that allowed them to easily narrow their audience and share posts with close friends (e.g., Snapchat). Therefore, adolescents who are in the phase of identity consolidation may benefit more by using platforms that make it easy to target posts to close friends.

Future Research Directions

Now that we have presented a theoretical model of adolescent identity development and discussed how the use of social media may facilitate or hinder the development, we suggest a few directions for future research. First, it is important to study how narrative identities typically develop during adolescence. Since many adolescents currently use social media for identity expressions (i.e., narrative identities), it is possible to examine the adolescent development of a narrative identity on social media (Granic et al., Reference Granic, Morita and Scholten2020). Although social media platforms have tightened restrictions on data access over the past few years (e.g., Facebook Business, 2018; Hemsley, Reference Hemsley2019), private data download options and application programming interfaces remain a viable avenue for collecting social media data for research (Lomborg & Bechmann, Reference Lomborg and Bechmann2014; Taylor & Pagliari, Reference Taylor and Pagliari2018). Adolescents’ social media posts can be analyzed with research methods that have been developed to study narrative identities (Adler et al., Reference Adler, Dunlop and Fivush2017). The narrative research framework specifies how to code narrative identities in terms of structure (e.g., coherence) and theme (e.g., agency, communion). New coding manuals need to be developed for themes that are relevant to adolescent identity development but not included in the framework (e.g., external versus internal focus). It is important to note, however, that people are not given narrative prompts to elicit detailed information on social media. Therefore, it may be necessary to use an additional method such as an interview to fully understand what is being expressed in social media posts. Alternatively, researchers may develop an application to add narrative prompts to social media posts. Once the methods are developed, researchers can conduct longitudinal studies to examine how the structure and theme of narrative identities change during adolescence.

Another important direction for future research is to study how the design and affordances of social media platforms affect key processes of identity development (introspection, elaboration, grappling, attention, and validation). For this line of research, it is important to first examine the unique affordances and features of different social media platforms. For example, researchers may assess the diversity of communities on social media platforms (see, e.g., Bisgin et al., Reference Bisgin, Agarwal and Xu2012; De Salve et al., Reference De Salve, Guidi, Ricci and Mori2018, for the methods). To assess the processes of identity development, researchers can code adolescents’ posts as well as others’ comments and reactions by using or adapting existing methods for studying these processes (e.g., Pasupathi & Hoyt, Reference Pasupathi and Hoyt2009; Pasupathi & Rich, Reference Pasupathi and Rich2005) or developing new ones.

However, like the analysis of narrative identities, the study of identity development processes may require more than social media data (especially introspection, which does not easily manifest itself). It would therefore be best to combine the coding of social media content with other methods that probe individuals’ experiences on social media (e.g., interviews). One useful approach is the stimulated recall method, in which interviews are conducted around objective data to aid the recollection of experiences associated with the data (Bloom, Reference Bloom1953). Using social media data as memory cues can facilitate the recollection of thoughts and feelings that occurred during the use of social media (Griffioen et al., Reference Griffioen, Van Rooij, Lichtwarck-Aschoff and Granic2020).

Finally, a worthwhile research direction is to develop applications that support key identity development processes and examine whether they facilitate adolescents’ identity development. For example, researchers may design an application based on the four dimensions of self-defining memories (Blagov & Singer, Reference Blagov and Singer2004) to help the meaning-making and organization of past experiences:

  • Affect: Users can rate the emotional valence and intensity of social media posts so that they can gain insights into what kind of events have an impact on them and the nature and degree of the impact.

  • Content: Users can assign value and commitment tags to their social media posts so that they can make explicit connections between their values and commitments and their life experiences.

  • Meaning: Social media posts are accompanied by narrative prompts so as to help users derive meaning from their experiences and construct a meaningful and coherent narrative identity.2

  • Time specificity: Users’ social media posts can be displayed in different time frames (e.g., a given moment in time, day, week, month, year, and life stage) so that users can create narratives in these different time frames and later integrate these narratives into a life story.

After applications are developed, researchers can conduct studies (e.g., randomized control trials) to evaluate their efficacy in facilitating adolescent identity development. It is recommended that researchers take person-specific effects into account when evaluating the effects of social media (e.g., see Valkenburg et al., Reference Valkenburg, Beyens, Pouwels, van Driel and Keijsers2021).

Conclusion

In this chapter we have suggested that it is important to study how the use of social media affects adolescent identity development in order to understand the mechanism of the impact of social media on adolescent mental health. We presented a model of the dual aspect of adolescent identity development – progression toward the formation of self-evaluated commitments and values and the construction of a coherent life story – and discussed how the use of social media may facilitate or hinder the key processes involved, namely introspection, storytelling, and dialogue. It was suggested that future research should devise methods for studying narratives on social media and discover how narrative identities develop during adolescence. We also suggested examining the design and affordances of social media platforms and how they affect the key processes of identity development. We hope that this chapter will provide a useful framework for future research on the impact of social media on adolescents and encourage media developers to design social media environments that support identity development.

4 Peer Relationship Processes in the Context of Digital Media

Samuel E. Ehrenreich

Peer relationships have always served an important role in adolescent development. The quality of peer relationships is a driving force in adolescents’ academic functioning (Wentzel et al., Reference Wentzel, Jablansky and Scalise2021), sense of self (Bellmore & Cillessen, Reference Bellmore and Cillessen2006), and mental health (La Greca & Harrison, Reference La Greca and Harrison2005). Furthermore, many – if not most – of the core developmental tasks that adolescents must traverse require navigating the peer context. Adolescents obviously cannot establish intimate peer relationships or explore romantic feelings and sexuality without engaging with their peers. Even experimenting with different versions of the self often requires feedback from peers to help understand how the external world will receive a potential internal self (Erikson, Reference Erikson1968).

Digital communication and social media have likely reshaped adolescents’ peer relationships and social environment more than any other force in the 21st century. Digital communication is adolescents’ preferred method for engaging with peers (Anderson & Jiang, Reference Anderson and Jiang2018), beyond even face-to-face interaction (Lenhart et al., Reference Lenhart, Ling, Campbell and Purcell2010). Nearly 90% of adolescents report using social media platforms every single day (Lenhart, Reference Lenhart2015), primarily to interact with the same peers and friends they interact in their offline lives. It is not surprising then that adolescents’ digital peer interactions are related to a range of outcomes similar to in-person peer interactions: self-concept and self-esteem (Steinsbekk et al., Reference Steinsbekk, Wichstrøm, Stenseng, Nesi, Hygen and Skalická2021), involvement in risk behavior (Ehrenreich et al., Reference Ehrenreich, Underwood and Ackerman2014), and mental health (Vannucci & McCauley Ohannessian, Reference Vannucci and McCauley Ohannessian2019). Digital communication is a critically important context that has transformed the way that the peer process unfolds and impacts adolescents (Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018a, Reference Nesi, Choukas-Bradley and Prinstein2018b).

This chapter will begin with an examination of the features of social media that make it such a powerful context in which peer interaction occurs, briefly reviewing the theoretical underpinnings of this context. We will review recent research on how three important peer constructs unfold and are shaped by digital media: peer influence, social connectedness (vs. isolation), and popularity and social status. We will then discuss challenges and opportunities for studying peer relationships in the context of digital media. Finally, we will conclude with a discussion of the future directions in this field.

Theoretical Considerations

Much of the early research examining how digital communication relates to peer relationships was guided by existing, “offline” developmental theory. This perspective coalesced in co-construction theory (Subrahmanyam et al., Reference Subrahmanyam, Smahel and Greenfield2006), which suggested that adolescents use social interaction in digital spaces as a means to explore the same developmental issues occurring in their offline lives. Accordingly, adolescents are active participants in the construction of the online content that they consume and create, building environments that can facilitate their developmental needs. Subrahmanyam and colleagues viewed these on- and offline environments as being “psychologically continuous” (Subrahmanyam et al., Reference Subrahmanyam, Reich, Waechter and Espinoza2008, p. 421). In line with this perspective, many early studies of peer relations in the digital sphere sought to examine whether important peer processes truly did translate between realms. For example, do adolescents’ offline social deficits translate into online spaces (i.e., the rich-get-richer hypothesis) or are online contexts used as a more comfortable space to compensate for their offline deficits (social compensation; Kraut et al., Reference Kraut, Patterson, Lundmark, Kiesler, Mukophadhyay and Scherlis1998, Reference Kraut, Kiesler, Boneva, Cummings, Helgeson and Crawford2002; Valkenburg & Peter, Reference Valkenburg and Peter2007)? Alternatively, considerable research examined the extent to which individuals who engaged in offline bullying behaviors or were subjected to offline victimization were also involved in these aggressive relations online (Kowalski et al., Reference Kowalski, Giumetti, Schroeder and Lattaner2014), and whether there was similar overlap in offline and online prosocial behavior (Wright & Li, Reference Wright and Li2011).

Co-construction was an important advancement, in that it promoted the application of existing developmental theory to the study of adolescents’ online interactions, which had previously functioned with a fractured combination of theories emerging from a variety of disciplines (see Underwood et al., Reference Underwood, Brown, Ehrenreich, Rubin, Bukowski and Laursen2018). However, co-construction theory placed great emphasis on the overlap between adolescents’ on- and offline worlds, highlighting that adolescents are creating these spaces in an effort to fulfill their offline developmental needs (Subrahmanyam et al., Reference Subrahmanyam, Reich, Waechter and Espinoza2008). Although co-construction does not suggest that these spaces are the same (despite being psychologically connected), little focus was placed on systematically identifying the ways in which digital communication functionally changes adolescents’ peer interactions. To bridge this gap, the transformational framework (Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018a, Reference Nesi, Choukas-Bradley and Prinstein2018b), sought to systematically identify specific ways that social media transforms peer experiences, proposing five specific methods. First, social media increases the frequency and immediacy of peer interactions, allowing (and encouraging) near-constant contact with peers. Second, and relating to this, social media also amplifies the demands of peer interactions, creating new expectations to be available and responsive to peers. Third, social media changes the qualitative nature and feel of peer interactions, for example by changing the access to various social cues, and placing a greater emphasis on quantitative peer metrics such as number of likes and followers. Fourth, social media affords youth new opportunities for compensating behaviors, such as the opportunity to maintain relationships despite physical distance. Finally, social media also provides adolescents with the potential to engage in entirely new social behaviors, such as virtually stalking romantic partners, or passively viewing the entire peer network.

Although this recently proposed framework has received limited empirical examination to date, initial findings examining the role of social media on women’s body image have generally supported the model (Choukas-Bradley et al., Reference Choukas-Bradley, Nesi, Widman and Higgins2019). Additional research is needed, but the transformation framework builds on existing developmental theory to highlight specific – and testable – ways that peer interactions should differ in, and be affected by, these digital contexts. Perhaps most importantly for its continued utility, the transformational framework highlights seven specific aspects of the social media environment (asynchronicity, permanence, publicness, availability, cue absence, quantifiability, and visualness) that transcend specific digital media platforms and tools (e.g., Facebook vs. Snapchat vs. text messaging). Given the incredible pace in which digital platforms rise and fall in popularity, emphasizing broader features of these platforms is critically important for a cohesive study of peer interactions in digital spaces over time.

Transformed Peer Constructs in Digital Communication

Guided by co-construction and the transformational framework, researchers have established the importance of digital communication in both promoting and inhibiting a variety of peer processes, and at times fundamentally transforming these processes altogether. In the following sections, we will review recent research on the role of social media on three of these important peer processes and constructs: peer influence, social connectedness versus isolation, and popularity/status. These sections will not serve as a comprehensive review but will instead highlight recent trends and future directions.

Peer Influence in Digital Realms

Susceptibility to peer influence peaks during the adolescent years (Steinberg & Monahan, Reference Steinberg and Monahan2007), due to an increased importance of peer relationships and status during this period (Prinstein & Dodge, Reference Prinstein and Dodge2008), as well as neurological development (Sommerville, Reference Somerville2013; Steinberg, Reference Steinberg2008). Adolescents look to their peers as informative models for what behaviors are considered acceptable and desirable (injunctive norms), and to assess the how frequent various behaviors are (descriptive norms; Kallgren et al., Reference Kallgren, Reno and Cialdini2000). Due to the highly public nature of many social media platforms, adolescents are able to spend hours examining the posted lives of their close friends and more distant peers. Because adolescents’ social media feeds display the content produced by their wide social networks, this could also serve to blur the line between proximal norms (their immediate friends) and more distal or global norms (peers in general). A great deal of research on peer influence has focused on how it can affect the development of problematic behaviors such as substance use (Geusens & Beullens, Reference Geusens and Beullens2017a, Reference Geusens and Beullens2017b). Adolescents who believe that their friends and peers are using substances (or hold positive views of substance use) are more likely to engage in this behavior themselves. Depictions of substance use are viewed on social media by both adolescents (Boyle et al., Reference Boyle, Earle, LaBrie and Ballou2017; Carrotte et al., Reference Carrotte, Dietze, Wright and Lim2016) and college-aged adults (Moewaka Barnes et al., Reference Moewaka Barnes, McCreanor, Goodwin, Lyons, Griffin and Hutton2016; Morgan et al., Reference Morgan, Snelson and Elison-Bowers2010), and these depictions in turn relate to individuals’ perception of injunctive norms (Boyle et al., Reference Boyle, LaBrie, Froidevaux and Witkovic2016; Yoo et al., Reference Yoo, Yang and Cho2016) and their own substance use (Geusens & Beullens, Reference Geusens and Beullens2017b). Substance use presentations on social media likely influence adolescents by changing their perception of the acceptability and prevalence of these behaviors. In one study, viewing peers’ posts about substance use improved the perceived desirability and positive expectancies of substance use behaviors (Huang et al., Reference Huang, Unger and Soto2014). Another study found that viewing friends’ substance use posts on social media predicted elevated drinking one year later, and this relationship was mediated by more positive injunctive peer norms about alcohol (Nesi et al., Reference Nesi, Rothenberg, Hussong and Jackson2017).

However, social media does not only influence adolescents by allowing them to observe their peers, but also permits adolescents to be observed by their peers as well. Adolescents are heavily influenced by the notion (accurate or inaccurate) that their activities are being viewed and judged by peers. Although the impact of the imaginary audience has been discussed for decades (Elkind, Reference Elkind1967), recent fMRI studies support the neurological underpinnings for this influence process. Simply being in the presence of peers increases adolescents’ susceptibility to peer influence by increasing functioning in the regions of the brain responsible for social cognition and reward seeking (primarily the amygdala, striatum, and prefrontal cortex; Somerville, Reference Somerville2013; Steinberg, Reference Steinberg2008). This increased focus on reward seeking in turn leads to greater risk-taking behavior (Chein et al., Reference Chein, Albert, O’Brien, Uckert and Steinberg2011; O’Brien et al., Reference O’Brien, Albert, Chein and Steinberg2011). In offline contexts, peer presence is a fairly objective variable (for both adolescents themselves and inquiring researchers), but many of the features of social media outlined in the transformation framework (Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018a) may amplify this experience. The availability and the publicness of social media means that peers can be “present” even when the adolescent is physically alone. Furthermore, the quantifiability of these networks, with a numeric quantity of followers and likes, could intensify peer influence. Recent fMRI studies have found that the neurological activation patterns underpinning peer influence when peers are physically present (Chein et al., Reference Chein, Albert, O’Brien, Uckert and Steinberg2011, Steinberg, Reference Steinberg2008) also occur when peers are “present” via Instagram (Sherman, Hernandez, et al., Reference Sherman, Hernandez, Greenfield and Dapretto2018; Sherman et al., Reference Sherman, Payton, Hernandez, Greenfield and Dapretto2016), and the impact of digital peer influence is stronger for adolescents compared to young adults (Sherman, Greenfield, et al., Reference Sherman, Hernandez, Greenfield and Dapretto2018).

The studies highlighted above suggest that social media can extend the reach of peer influence beyond physical presence and interaction with peers. Future research can leverage the networked data available on these platforms to better understand the role of proximal and distal peers in influencing adolescents’ behavior, and to operationalize different levels of peer connection and degrees of separation from each other in more detailed ways. For example, frequency of communication with a peer or even frequency of viewing a peer’s posts might objectively and accurately assess proximity to that peer. Alternatively, metrics used in social network analyses such as network closure and centrality can be used to more clearly define proximal and distal peers (Hanneman & Riddle, Reference Hanneman, Riddle, Scott and Carrington2011). This would allow researchers to go beyond simply asking adolescents to identify and rate their friends and peers, to directly assess with whom an adolescent digitally interacts and is connected. Directly assessing interactions (and observation) at the network level could greatly enhance our understanding of peer influence for a variety of important variables such as mental health, academic performance, and body image issues.

Social Connectedness and Isolation via Social Media

The role of social media in promoting (or inhibiting) social connectedness has received increasing research interest over the past several years. Social connectedness and a feeling of belonging is one of the primary benefits of peer relationships during adolescence, promoting positive psychosocial outcomes (Bradley & Inglis, Reference Bradley and Inglis2012) and protecting against both externalizing and internalizing problems (Newman et al., Reference Newman, Lohman and Newman2007). As social media and digital communication increased in popularity, there was a great deal of speculation about whether these technologies would foster intimacy and connection with peers, or if the reductions in face-to-face interaction would actually diminish adolescents’ sense of belongingness with peers (Allen et al., Reference Allen, Ryan, Gray, McInerney and Waters2014). Some proposed that specific features of social media would provide opportunities to better connect with peers. In a series of interviews conducted with adolescents, Davis (Reference Davis2012) identified that frequent communication with friends through a variety of digital platforms promoted a sense of closeness with these peers. The ability to connect with peers despite physical distance is identified by adolescents as one of the primary benefits of digital communication (Ling, Reference Ling, Harper, Palen and Taylor2005). Indeed, adolescents exchange a great deal of emotionally supportive communication via social media (Siriaraya et al., Reference Siriaraya, Tang, Ang, Pfeil and Zaphiris2011), using these platforms to reach out to peers in times of need (Ehrenreich et al., Reference Ehrenreich, Beron, Burnell, Meter and Underwood2020).

Beyond using social media to directly interact with peers, there is also some evidence that posting broadly to social media platforms without directly connecting with a specific peer (such as a tweet or a status post on Facebook) can reduce loneliness in undergraduate samples (große Deters & Mehl, Reference große Deters and Mehl2013; Lou et al., Reference Lou, Yan, Nickerson and McMorris2012). These findings highlight that the availability of the peer network that social media affords adolescents translates into increases in connection and belongingness, and reductions in loneliness. Indeed, a meta-analysis examining 63 studies found that social media use was positively correlated with perceived social resources from peers (Domahidi, Reference Domahidi2018). Interestingly, a recent study examining specific features of social media platforms found that image-based platforms in particular (e.g., Instagram and Snapchat) reduced users’ loneliness (Pittman & Reich, Reference Pittman and Reich2016). The authors speculate that the emphasis on images facilitates the sense of a “social presence” with peers that is better able to promote connection, aligning with the perspective that the visualness of social media (Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018a) may be an important feature for subsequent research into the role of social media in connection.

In contrast to the potential benefits of social media on adolescents’ peer connection, a separate body of research has suggested that smartphones and social media use are actually reducing social connection and well-being, and account for overall increases in social isolation and loneliness among adolescents (Twenge, Reference Twenge2019). Population-level studies have indeed identified increasing trends in both suicidality and depression over the past decade (Mojtabai et al., Reference Mojtabai, Olfson and Han2016) that coincided with similar rises in cellphone ownership and social media use (Twenge et al., Reference Twenge, Joiner, Rogers and Martin2018). One meta-analysis found that social media use does indeed correlate with perceived loneliness (although the authors suggest that loneliness predicting social media use is the most likely direction of effect; Song et al., Reference Song, Hayeon and Anne2014). One large-scale cross-sectional study of young adults found that social media usage was a significant predictor of social isolation (Primack et al., Reference Primack, Shensa and Sidani2017), and a micro-longitudinal study also found that time spent on social media predicts momentary feelings of social isolation (Kross et al., Reference Kross, Verduyn and Demiralp2013). Furthermore, a few experimental studies have also supported the hypothesis that social media causally predicts maladjustment. College students who were instructed to limit their social media use to no more than 30 minutes per day reported lower levels of depression and loneliness compared to the control group (Hunt et al., Reference Hunt, Marx, Lipson and Young2018). Similarly, individuals randomly assigned to abstain from Facebook for one week reported being happier and less depressed by the end of the week (Tromholdt, Reference Tromholt2016).

Although the immediate and constant connection that social media provides is appealing to adolescents (Davis, Reference Davis2012), there is concern that time spent on these digital platforms comes at the cost of more intimate and socially valuable face-to-face time (Kraut et al., Reference Kraut, Patterson, Lundmark, Kiesler, Mukophadhyay and Scherlis1998). The conflicting evidence on the role of social media in supporting or inhibiting social connection likely reflects methodological limitations for disentangling direction of effect (but see George et al., Reference George, Beron, Vollet, Burnell, Ehrenreich and Underwood2021 and Twenge, Reference Twenge2019 for contrasting perspectives on this). However, it also likely reflects the reality that the way adolescents are using these technologies may be more important than the overall time spent online. In particular, it appears passive social media use (time spent scrolling through peers’ posts without actually interacting or engaging with peers) may be especially harmful for adolescents’ well-being and sense of connection, compared to actively engaging with peers via social media. Time spent passively viewing peers’ social media content indeed predicts reductions in perceived peer support (Frison & Eggermont, Reference Frison and Eggermont2015), increases in social loneliness (Amichai-Hamgurger & Ben-Artzi, Reference Amichai-Hamburger and Ben-Artzi2003; Matook et al., Reference Matook, Cummings and Bala2015) and a sense of disconnection from peers (Amichai-Hamgurger & Ben-Artzi, Reference Amichai-Hamburger and Ben-Artzi2003) that likely grows out of feelings of envy and negative social comparison (de Vries et al., Reference de Vries, Möller, Wieringa, Eigenraam and Hamelink2018; Vogel et al., Reference Vogel, Rose, Okdie, Eckles and Franz2015; Weinstein, Reference Weinstein2017).

In contrast to the consistently negative correlates of passive social media use, active social media use (posting and directly interacting with peers) appears to have much more positive outcomes. Adolescents’ public Facebook posts elicit positive feedback from peers, which in turn increases the perception of peer support (Frison & Eggermont, Reference Frison and Eggermont2015). Similarly, experimentally increasing the frequency of posting publicly on Facebook reduced loneliness among college students (große Deters & Mehl, Reference große Deters and Mehl2013). Social media can also facilitate more private, dyadic interactions among peers, which in turn predicts social connection and support (Frison et al., Reference Frison, Bastin, Bijttebier and Eggermont2019; Frison & Eggermont, Reference Frison and Eggermont2015). It is not surprising that the opportunities for actual peer interaction (active use) promote feelings of connection and support among adolescents; indeed this was identified by adolescents as a primary benefit (Davis, Reference Davis2012). However, the conflicting findings between social media contributing to connection versus isolation highlights the importance of how adolescents are using these media. Future research must continue to focus on the specific online behaviors and usage patterns that foster connection, rather than simply assessing the amount of time spent using these platforms. The transformational framework model (Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018a) may be especially useful in disentangling the conflicting findings that have emerged in this research area. By focusing on the specific features of social media platforms that may be shaping peer interactions in these contexts, researchers can better understand what promotes a sense of connection and peer support, and what may undermine it.

Popularity and Social Status

Because of its highly public nature and constant availability, social media may be especially important in shaping adolescent social status (Nesi & Prinstein, Reference Nesi and Prinstein2019). Although social status has always been an important component of adolescent peer relationships (Harter et al., Reference Harter, Stocker and Robinson1996), social media both intensifies that importance and salience of peer status, and also provides new tools for managing and promoting status (Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018b). The quantifiability of social networks makes social media especially important for adolescents’ perceptions of status. Adolescents are highly aware of a variety of social media metrics assessing popularity (e.g., number of friends, number of likes or retweets; Madden et al., Reference Madden, Lenhart and Cortesi2013).

Indeed, the preoccupation with popularity on social media may have reframed adolescents’ traditional desire for popularity into aspirations for fame and stardom. Content analysis of movies and television viewed by adolescents has found that fame is increasingly portrayed as an important – and achievable – goal (Uhls & Greenfield, Reference Uhls and Greenfield2012), and adolescents who use social networking sites more frequently report a greater emphasis on the value of fame (Uhls et al., Reference Uhls, Zgourou and Greenfield2014). This emphasis on fame is somewhat attributable to the rise in popularity of reality television, wherein “ordinary people” ostensibly become famous for simply living their day-to-day lives (Rui & Stefanone, Reference Rui and Stefanone2016). But adolescents are also highly cognizant of the potential to achieve celebrity simply by acquiring enough social media followers (e.g., “Instagram famous”; Marwick, Reference Marwick2015).

Although social media has made peer status and popularity much more salient, it has also provided a variety of tools adolescents can use in their attempt to improve their status. Prior to the advent of social media, many adolescents no doubt spent their free time envisioning moving up the social hierarchy. However, with the help of smartphones and social networking sites, adolescents can actively work toward improving their number of friends, and curating their self-presentation at all times. Adolescents are quite strategic in leveraging social media to promote a positive and popular image. Many adolescents go to great lengths to ensure that their self-presentation on social media receives positive peer response, including taking numerous photos to select the best image for posting (Yau & Reich, Reference Yau and Reich2019), heavily editing photos to present an attractive image (Bell, Reference Bell2019), curating the activities they disclose to create a fun and glamorous identity (Fardouly & Vartanian, Reference Fardouly and Vartanian2016), and timing posts to maximize peer likes (Nesi & Prinstein, Reference Nesi and Prinstein2019). Indeed in her analyses of adolescents’ digital presentations, Marwick (Reference Marwick2013) suggests adolescents are engaging in “self-branding,” designed to market themselves using techniques similar to consumer products.

Although social media may provide a variety of new tools for managing one’s social status, that does not mean that all adolescents leverage social media to achieve higher status. Using social media in ways that will promote one’s social status requires a significant amount of social competence (Reich, Reference Reich2017) and a great deal of effort (Yau & Reich, Reference Yau and Reich2019). Popular adolescents are more likely to engage with their peers in ways that will promote their existing status, including both positive and aggressive behaviors. Furthermore, popular adolescents who are better able to self-monitor and regulate the online interactions are less likely to be the target of cybervictimization (Ranney & Troop-Gordon, Reference Ranney and Troop-Gordon2020).

Opportunities and Challenges for Studying Peer Relationships in Digital Communication

As social media increases as an important context for adolescents to interact with their peers, it presents both opportunities and challenges for researchers seeking to better understand peer relationships. Perhaps the greatest advantage of social media is that it permits researchers to connect with adolescents where their peer interactions are unfolding. While observing peer interactions used to require artificial lab settings (Piehler & Dishion, Reference Piehler and Dishion2007) or naturalistic observation that was restricted in time and location (Snyder et al., Reference Snyder, McEachern and Schrepferman2010), researchers can now potentially observe peer interactions in digital spaces unobtrusively for extended periods of weeks, months, or years (Hendriks et al., Reference Hendriks, Van den Putte, Gebhardt and Moreno2018; Underwood et al., Reference Underwood, Rosen, More, Ehrenreich and Gentsch2012). Furthermore, because much of adolescents’ digital communication is centered around their smartphones, a variety of additional data collection technologies can be connected with peer relationships and interactions, including ecological momentary assessment (Duvenage et al., Reference Duvenage, Uink, Zimmer‐Gembeck, Barber, Donovan and Modecki2019), geolocation (Boettner et al., Reference Boettner, Browning and Calder2019), and even physical functioning such as sleep patterns (George et al., Reference George, Rivenbark, Russell, Ng’eno, Hoyle and Odgers2019). These technologies provide researchers with a unique opportunity to stitch together a more comprehensive understanding of how peer relationships are impacting adolescents’ functioning and development.

Although the potential for these research methods is truly exciting, they are not without challenges and risk. First, there are important ethical considerations for researchers to capture the volume of data available in adolescents’ digital spaces. Although adolescents seem fairly comfortable with digital observation (Meter et al., Reference Meter, Ehrenreich, Carker, Flynn and Underwood2019), capturing digital communication nonetheless involves novel ethical considerations. Because this data collection can be conducted subtly from smartphones and social media apps, it is important that researchers clearly explain the details of digital data collection. Similarly, since social media data is inherently networked information, challenges arise for navigating when it is necessary to obtain peer consent (and whether that is even possible). This may require a dialogue with IRBs and granting institutions to better reflect the digital contexts in which adolescents live their lives. With tens of millions of adolescents permitting third parties to observe their social media data, these research activities are likely the very definition of minimal risk (see Ehrenreich et al., Reference Ehrenreich, George, Burnell and Underwood2021 for a discussion about this).

Another challenge for researchers is understanding the hidden, guiding hand of the algorithms that decide what is presented on social media platforms. These algorithms constantly evaluate the adolescents’ social media behavior to provide a stream of content tailored to the adolescent (and the marketing forces underlying many of these platforms). The role of these artificial intelligence and machine learning algorithms obfuscates peer processes occurring in these platforms. For example, one long-running research inquiry has examined whether adolescents’ similarity to peers is best explained by socialization (learning how to behave from our peers) or selection (choosing peers who behave as we do). Evidence suggests that both of these processes likely work in tandem: adolescents select peers who are similar to them, who in turn further socialize their attitudes and behaviors. However, on social media these two processes become further intertwined (and blurred), as the content an adolescent views and posts themselves will in turn affect who and what is highlighted in their social media feeds. In this way, the content that is socializing the adolescent is also being used to select the peers who will be suggested to them or featured on their feed, and the selection of this network is in turn dictating what content will be presented (and will thus socialize the adolescent further). And all of these “decisions” are being conducted by computer algorithms that are likely hidden to the adolescent. Indeed, much of TikTok’s explosion in popularity during 2020 is attributed to the advanced artificial intelligence recommendation engine that rapidly tailors what videos are suggested based on the user’s previous preferences (see Wang, Reference Wang2020 for an overview of this technology). Much of the research outlined above highlights investigations into how social media features and content impact adolescents peer relationships. But why adolescents are exposed to features and content (e.g., why this specific video is presented at the top of their feed) is being guided by algorithms that are likely poorly understood by both adolescents and developmental scientists.

Future Directions

In their presentation of the transformational framework, Nesi and colleagues (Reference Nesi, Choukas-Bradley and Prinstein2018a, Reference Nesi, Choukas-Bradley and Prinstein2018b) highlight seven features of the social media context that are important to understanding how peer relationships operate in these environments (asynchronicity, permanence, publicness, availability, cue absence, quantifiability, and visualness). Future research must move away from examining specific social media platforms, and instead focus on their features. Not only do social media platforms rise and fall in popularity, but they also change their form and features over time. Not only is Facebook less popular among adolescents than it was in 2012 (Rideout & Robb, Reference Rideout and Robb2018), but the platform itself is also quite different, with new features constantly being added. By focusing on features of social media that can be assessed on a variety of platforms (e.g., the emphasis on visual content versus textual, the degree of asynchronicity; Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018a), researchers can better understand how the broader social media context is shaping adolescents’ peer relationships, and these impacts can be assessed more consistently across time.

But the importance of these features of the social media context may not just be limited to assessing the social media platforms themselves. Perhaps some (or all) of these features of the context are now reflected in fundamental changes in the relationships themselves. For example, prior to the advent of social media, moving into new stages of life often meant losing contact with peers from previous stages. Although an adult may have retained friends from middle or high school, it was perhaps unlikely that they kept tabs on the broader peer network from those years. However, with Facebook, Instagram, and other social networking sites, it is quite common for individuals to maintain a (perhaps tenuous) connection with these earlier peer networks. Although the transformational framework suggests that a feature of the Facebook context is its permanence (e.g., photos and subsequent comments are retained indefinitely), by extension, relationships themselves may now reflect this feature (the relationship itself is now retained indefinitely).

It is possible that other features of social media may be redefining the features of peer relationships as well. For example, perhaps the cue absence permitted in social media is redefining how adolescents want to experience all relationship interactions. Alternatively, perhaps the publicness of social media has fostered the perception that relationships themselves should be experienced publicly. If this were the case, it would challenge the conventional adolescent developmental task of navigating intimate relationships traditionally characterized as a dyadic process. Similarly, there has been a great deal of concern about how digital communication may be undermining youth’s development of more general social skills, such as navigating small talk and interpersonal interactions (Turkle, Reference Turkle2012). Whereas periods of downtime (e.g., waiting for a class to begin, standing in line at the supermarket) used to be opportunities to strike up a conversation with the stranger next to you, these moments are now often spent checking in with peers on one’s phone. A student of mine once shared that she used her phone to avoid getting drawn into a conversation with her classmates, because she worried she wouldn’t be able to end the conversation if it was awkward or boring. While the asynchronicity and availability of digital communication may permit adolescents to have social interactions on their own terms, perhaps it comes at the cost of learning to navigate challenging, awkward (and even boring) interactions. The seven features of social media outlined by the transformation framework (Nesi et al., Reference Nesi, Choukas-Bradley and Prinstein2018a, Reference Nesi, Choukas-Bradley and Prinstein2018b) provide an important advancement for the study of adolescents’ interactions occurring via digital media, but they also provide guidance for future research seeking to understand how peer relationships themselves are fundamentally changing.

Finally, future research must increasingly focus on the behaviors and processes that are occurring in these platforms. In many ways, researchers’ initial focus on the quantity of social media use has obfuscated our understanding of these contexts (such as the conflicting associations between social media and subsequent loneliness and mental health). Current research is illuminating the fact that time spent on social media is less important than how adolescents are using these platforms (e.g., Swirsky, Rosie & Xie, Reference Swirsky, Rosie and Xie2021). Researchers must continue to move away from overly simplistic metrics of social media use. Examinations about the amount of time spent on social media should be reframed into how time is spent on social media. Evaluating the number of friends and followers is likely less important than evaluating the interactions (and observation) of those peer networks. Luckily social media platforms provide a unique opportunity to naturalistically observe adolescents in these more nuanced ways.

Conclusion

Social media platforms have become an increasingly important context for adolescents’ peer relationships. These platforms are reshaping the way that adolescents interact with and observe their peers. In many ways, social media has accomplished what social scientists have sought to do for years: it has established a platform that makes peer relationships quantifiable, networked, available to outside observers, and permanent so interactions can be scrutinized and analyzed after the fact. It is perhaps somewhat ironic that the features that make these platforms ideal for studying peer relationships are driving many of the changes occurring in these relationships. The publicness of these data allows researchers to observe teens more easily but does it come at the cost of intimate connections with peers? The quantifiability of social media may allow researchers to better understand social status hierarchies. But in doing so, does it change what these hierarchies mean to adolescents? Researchers are now presented with the opportunity to leverage these powerful new social tools to better understand adolescents’ relationships but must simultaneously address how these tools are shifting how these relationships unfold and impact adolescents.

5 Digital Media and the Developing Brain

Michelle Chiu and Jason Chein

The pervasiveness of modern digital media in the lives of children and teens has raised important questions about how exposure to, and involvement with, such media might interact with the developing brain. The framing of these questions reflects differing implicit beliefs about the direction of causality in this relationship. Some questions assume that facets of brain development can predispose youth to digital media involvement. It is tempting to ask, for example, whether maturational processes taking place within the brain might prejudice a given age group to become especially enmeshed with digital media, or whether individuals whose brain development lags behind (or is relatively more precocious than) that of their peers also incur greater vulnerability to the consequences of digital media use. Conversely, questions can be posed with the underlying assumption that digital media environments can themselves impact subsequent brain development. We might wonder, for instance, whether digital media experiences have the potential to fundamentally “rewire” the cabling patterns in developing brains, or to stunt or alter the normative developmental processes that lead to a “mature” functional brain. Seeking answers to questions like these carries obvious importance in informing how we, societally and individually, approach the introduction and management of digital media in the lives of our children and teens.

In this chapter, we explore evidence that shapes our current understanding of the relationship between the developing brain and digital media experiences. Although there are many tools that can be applied to the question of how the developing brain affects, and is affected by, digital media behaviors, noninvasive magnetic resonance imaging (MRI) methods have produced many of the key insights, and are the central source of evidence discussed in this chapter. Broadly, MRI methods can be broken down into structural and functional approaches. Structural MRI methods are used to characterize the static anatomical and structural properties of an individual’s brain, and include morphometric methods that detail the specific qualities (e.g., thickness, volume) of the gray matter that comprises the brain’s outer cortex and major subcortical nuclei, and diffusion-weighted imaging methods that can characterize the cabling patterns formed by the brain’s white matter pathways. Meanwhile, functional MRI methods, including task-based functional MRI (fMRI) and intrinsic connectivity approaches such as resting-state fMRI (rsfMRI), allow us to observe dynamic temporal variation in the activation of individual brain regions, and in the coactivation of regions cooperating as part of interconnected brain networks. The evidence from these MRI-based approaches can be considered alongside data derived from complementary methods that render a sharper view of the temporal structure and dynamics of key neural events, especially electroencephalography (EEG).

Combining evidence obtained from these different modalities allows us to consider where and how the findings coalesce, and to evaluate the extent to which various perspectives on the relationship between brain development and digital media behaviors are supported. We focus on three emergent perspectives on the developmental factors that drive, and may be influenced by, digital media habits, and attempt to link these perspectives to evidence on the specific brain networks implicated in these facets of development. One perspective derives from theoretical and empirical work suggesting that the differences in the ability to exert self-regulatory control might account for variation in digital media involvement. Broadly, the idea is that those who struggle to control their thoughts, actions, and the orientation of their attention may be more prone to form digital media habits and be more vulnerable to any impacts of the behavior (Brand et al., Reference Brand, Young and Laier2014; Wei et al., Reference Wei, Zhang, Turel, Bechara and He2017; Wilmer et al., Reference Wilmer, Sherman and Chein2017). As we will detail in the next section of the chapter, there are specific brain regions thought to support the capacity for self-regulatory control, and evidence suggesting that the structure and function of these regions might be relevant in the development of digital media habits. A second perspective on digital media involvement focuses on variation across age groups and individuals in the valuation of, and responsiveness to, environmental rewards. Under this view, normative developmental shifts in reward-relevant processes may introduce periods of particular susceptibility to the appetitive, novel, and arousing properties of digital media interactions, especially for those who possess (or who come to develop) a particularly acute sensitivity to those rewards (Firth et al., Reference Firth, Torous and Stubbs2019). Once again, this perspective orients us to the specific brain regions we know to be involved in the coding of reward value and in processing the outcomes of pursuing (or not pursuing) environmental rewards. A third perspective arises from evidence highlighting conspicuous developmental shifts in the importance of nonfamilial social relationships, and associated changes in orientation and response to social influence (Blakemore, Reference Blakemore2018; Mills et al., Reference Mills, Lalonde, Clasen, Giedd and Blakemore2014; Sutter et al., Reference Sutter, Zoller and Glätzle-Rützler2019). Since digital media, and especially social media, have become such an important source of socially relevant information, some argue that developmental changes in the structure and function of the “social brain” might be especially important for understanding what motivates digital media behaviors, and how they might impact subsequent brain development (Meshi et al., Reference Meshi, Turel and Henley2020).

Importantly, the brain systems that support control, reward, and social information processes are thought to follow distinct trajectories of development. Whereas the mechanisms involved in self-regulatory control mature in a gradual and protracted manner across the period from middle childhood into early adulthood, the brain regions that subserve valuation of, and sensitivity to, rewards are thought to undergo more rapid reconfiguration during early adolescence, in response to the hormonal changes of puberty (Sisk & Zehr, Reference Shulman, Smith and Silva2005; Smith et al., Reference Smith, Chein and Steinberg2013; Spear, Reference Spear2010). This asynchronous developmental timing has important implications for how these systems interact with one another, and may be fundamental to understanding their roles in relation to emerging digital media behaviors. Meanwhile, regions associated with social information processing evince mixed developmental timing patterns (Atzil et al., Reference Atzil, Gao, Fradkin and Barrett2018; Kilford et al., Reference Kilford, Garrett and Blakemore2016; Mills et al., Reference Mills, Siegmund and Tamnes2021; Richardson et al., Reference Richardson, Lisandrelli, Riobueno-Naylor and Saxe2018), with some areas showing marked change in periods of childhood and adolescence, and others showing a more protracted developmental trajectory resembling that of self-regulatory control regions.

While this is a quickly advancing area of scientific inquiry, and the tools of modern neuroimaging have afforded valuable insights into the structure and function of the developing human brain, the reader should be warned from the outset that conclusive answers to the types of questions we posed in the opening paragraph of this chapter are still on the horizon. Rather, perspectives on how digital media experiences might interact with brain development currently derive from only a sparse corpus of fundamentally limited research. Perhaps the most obvious limitation is that very few studies are able to address the directionality of observed relationships. This is because, at present, the vast majority of relevant data originates from purely cross-sectional or correlational work, and very few true experiments or longitudinal studies exist to more adequately clarify causal patterns. It is worth noting, however, that the patterns of association and group differences observed in correlational and cross-sectional studies do help to guide alternative causal hypotheses, and the absence of predicted patterns can serve as key counterevidence against causal claims. Another clear limitation of the literature is that surprisingly few neuroinvestigative studies explore the brain correlates of digital media habits as they arise during the course of development. Rather, most of the evidence derives from studies of brain–behavior relationships observed at a relatively late point in development (in late adolescent and young adult cohorts). This state of affairs is due, in part, to the fact that many digital media behaviors (e.g., smartphone and social media account ownership, online gaming) often only begin to take hold in middle to later adolescence (Lauricella et al., Reference Lauricella, Cingel, Beaudoin-Ryan, Robb, Saphir and Wartella2016), and also to the inherent challenges involved in collecting neuroimaging data from younger participants (e.g., excessive movement and difficulty with task compliance). In our consideration of the brain systems implicated in digital media habits, we therefore rely primarily on the findings from later developmental periods, with the hope that this work contains reliable clues to how digital media experiences might interact with earlier brain development. Finally, as is discussed elsewhere in this handbook, the ever-changing technological landscape makes it difficult to conduct studies on digital media and the brain in a way that sufficiently addresses developmental cohort effects (i.e., the specific digital milieu available to a given developing cohort) and that anticipates consequential changes in the character and function of the digital media environment (e.g., the introduction of new social media forms). As such, much of what we can conclude to date is based on untested assumptions about the stability of observed brain–behavior relationships across varying digital media modalities and ecosystems.

With these caveats in mind, we can consider what the brain science tells us about the brain–behavior relations that surround digital media use. In the sections that follow, we consider, in turn, whether the brain regions and networks implicated in control, reward, and social processing are specifically relevant to digital media experiences. For each perspective, we weigh whether the extant neuroinvestigative evidence is corroborative or not, and consider how the specific pattern of evidence might sharpen or refine current explanatory theories. Rather than attempt an exhaustive review, we walk the reader through some seminal and informative findings, focusing first on studies from mostly nondevelopmental samples of adults, and then visiting the sparse but instructive patterns that have emerged in the developmental neuroscientific literature.

Digital Media and the Brain’s Control and Attention Networks

A growing body of work points to associations between digital media behaviors and the capacity for top-down self-regulatory control over thoughts, emotions, and behavior. Behavioral scientists often subdivide this skillset into separate psychological constructs with different labels (e.g., executive functioning, response inhibition, working memory, attention control, emotion regulation), and use a varied array of tasks and surveys to index its subcomponents. The general finding from across behavioral studies is that groups (and individuals) who demonstrate a weaker capacity for control also tend to exhibit higher levels of digital media use and more problematic involvements (e.g., excessive or addiction-like1 use) with various media forms. This is presumably because the inability to reliably exert control makes one more prone to impulsive engagement with digital media (e.g., frequent phone checking), greater attentional distractibility in response to media-associated cues (e.g., notifications), and greater difficulty with sustaining goal-relevant behaviors in the presence of digital media (Ward et al., Reference Ward, Duke, Gneezy and Bos2017). In our own lab, we have found that poorer performance on self-report and behavioral measures of response and impulse control is associated with increased smartphone and social media use habits among young adults (Wilmer & Chein, Reference Wilmer and Chein2016) and that early signs of this relationship are already present in much earlier stages of development (i.e., in a cohort of 6- to 8-year-olds, unpublished data). Several other studies detail similar relationships between poorer cognitive and attentional control and varying forms of digital media involvement, including greater social media use (Alloway & Alloway, Reference Alloway and Alloway2012), internet dependency (Choi et al., Reference Choi, Park and Roh2014; Dong et al., Reference Dong, Zhou and Zhao2011), increased media-multitasking (the concurrent use of alternate digital media modalities; Baumgartner et al., Reference Baumgartner, Weeda, van der Heijden and Huizinga2014; Lopez et al., Reference Lopez, Heatherton and Wagner2020; Minear et al., Reference Minear, Brasher, McCurdy, Lewis and Younggren2013; E. Ophir et al., Reference Ophir, Nass and Wagner2009), and excessive smartphone habits (Liebherr et al., Reference Liebherr, Schubert, Antons, Montag and Brand2020), at various points in development.

Given the apparent links between the behavioral expression of self-regulatory control processes and a range of digital media behaviors, an obvious place to begin looking for brain–behavior relationships tied to digital media use is within the brain regions and networks thought to support control processes. Considerations of where “control” arises in the brain often emphasize the lateral prefrontal cortex, but a more extensive characterization of how control is enacted might consider three complementary brain networks (Cole & Schneider, Reference Cole and Schneider2007; Dixon et al., Reference Dixon, De La Vega and Mills2018; Dosenbach et al., Reference Dosenbach, Fair, Cohen, Schlaggar and Petersen2008; Gratton et al., Reference Gratton, Sun and Petersen2018). The most prominent of these networks, the fronto-parietal “executive” network (FP; see Figure 5.1), is comprised of the dorsolateral prefrontal cortex (dlPFC, found in the middle frontal gyrus), the posterior parietal cortex (PPC, spanning the supramarginal gyrus and neighboring cortex extending into the intraparietal sulcus), and a dorsomedial prefrontal (dmPFC) region covering the dorsal extent of the anterior cingulate cortex (dACC) and extending into the midline superior frontal gyrus. While the FP network is thought to orchestrate the initiation and adjustment of control, the cingulate component of this network, along with a mid-anterior cingulate (mACC) area found slightly more rostral (in front of) and inferior to (below) the dACC, also functions as a hub region that dynamically coordinates its activity with the bilateral operculum (including the anterior insula and the neighboring posterior segment of the inferior frontal gyrus) to form a cingulo-opercular network (CO; see Figure 5.1). The CO network is thought to drive sustained control over behavior, and to orchestrate reactions to salient (goal-relevant or attention-grabbing) external and interoceptive (coming from inside the body) events, thus giving the network its alternative name – the “salience” network (Menon, Reference Menon and Toga2015). The ability to intentionally orient attention toward specific external and internal (mental) events is also known to involve an additional attention control network that has been dubbed the dorsal attentional network (also shown in Figure 5.1), which includes superior portions of the bilateral parietal association cortex (superior parietal lobule) as well as the bilateral frontal eye fields (found where the middle frontal gyrus intersects with the precentral gyrus). The brain regions encompassed in these control networks are generally understood to undergo a gradual, and particularly protracted, period of maturation that extends from childhood into at least the mid-twenties, which may explain why the ability to exert self-regulatory control over arousing and distracting stimuli is not fully formed until young adulthood (Sherman et al., Reference Sherman, Payton, Hernandez, Greenfield and Dapretto2016).

Figure 5.1 Visualization of regions comprising the brain networks thought to be associated with digital media behaviors. Key control regions are shown for the fronto-parietal “executive” network, the cingulo-opercular control network, and the dorsal attentional network, including the frontal eye fields (FEF) and superior parietal lobule (SPL). Also shown are regions strongly implicated in reward processing and those thought to be connected to social processing in the brain.

These control and attentional networks in the brain may be important to the manifestation of digital media behaviors. Some research implicating the neural correlates of control in digital media habits relies on basic structural MRI measurements of regional gray matter volume/density in the brain. Various studies on excessive internet use and online gaming behaviors, for example, offer evidence of reduced gray matter in key regions of the FP and CO networks, including the lateral prefrontal cortex (Q. He et al., Reference He, Turel, Wei and Bechara2020; Yuan et al., Reference Yuan, Qin and Wang2011), the dACC (X. Lin et al., Reference Li, Li and Yang2015; Yuan et al., Reference Yuan, Qin and Wang2011), and the insula (Turel et al., Reference Turel, He, Wei and Bechara2020); though studies in these populations occasionally show the opposing pattern of relationship (cf. Li et al., Reference Li, Li and Yang2015). Decreased gray matter volume has also been observed in the lateral prefrontal cortex, dACC, and anterior insula of individuals with smartphone “addiction” (Horvath et al., Reference Horvath, Mundinger and Schmitgen2020; Y. Wang et al., Reference Wang, Zou, Song and Xu2016), and in the dACC of individuals exhibiting a strong tendency to engage in media-multitasking (Loh & Kanai, Reference Loh and Kanai2014). While some investigators interpret these associations as evidence of consequential long-term impacts of digital media habits on the structural maturation of the brain’s control centers, such causal conclusions are simply untenable on the basis of this correlational evidence (e.g., we cannot know whether different habits lead to differentiated brain maturation, or whether different brains lead to differentiated habits). Moreover, it is challenging to translate evidence of altered structure into functional terms. For instance, while reduced gray matter volume does at times coincide with disrupted functioning, decreases in gray matter over the course of adolescent development are also thought to reflect the normative and desirable removal of unneeded neuronal connections through synaptic pruning (Gogtay & Thompson, Reference Gogtay and Thompson2010).

To try to clarify the nature of the relationship, we can consider evidence from studies of brain activity and connectivity, many of which highlight the same control-relevant brain regions. One early study on the functional correlates of internet gaming (Sun et al., Reference Sun, Ying and Seetohul2012) found that visual cues designed to elicit cravings among heavy video-gamers induced activation of the bilateral dlPFC and dACC. Subsequent work found that heavy media-multitasking was linked to both poorer performance and relatively increased right lateralized PFC activity when attempting an attentionally demanding task, which suggested that media-multitaskers might experience more difficulties when recruiting cognitive control resources (Moisala et al., Reference Moisala, Salmela and Hietajärvi2016). More recently, similarly aberrant lateral and dorso-medial PFC activation was reported in association with excessive smartphone usage (Schmitgen et al., Reference Schmitgen, Horvath and Mundinger2020), with smartphone addicts exhibiting increased activity in these areas when viewing smartphone-relevant visual cues (perhaps indicative of a need for greater effort in order to inhibit cue-related responses). Studies examining functional connectivity within and between the brain’s control networks provide further clarification of the relationship between control and digital media behaviors. The same group of smartphone addicts studied in Schmitgen et al. (Reference Schmitgen, Horvath and Mundinger2020) also evinced weaker coordination between the dmPFC and the left PPC, and between the anterior insula and the right lateralized PPC (Horvath et al., Reference Horvath, Mundinger and Schmitgen2020). Other recent work in heavy and excessive smartphone users has likewise indicated weaker intra-network connectivity in the FP and CO networks (Chun et al., Reference Chun, Park and Kim2020), and decreased functional (Chun et al., Reference Chun, Park and Kim2020) and structural (Wilmer et al., Reference Wilmer, Hampton, Olino, Olson and Chein2019) connectivity between key centers of the brain’s control networks and the ventral striatum (VS), a region of the brain where mesolimbic dopamine is released to signal the value of potential rewards. Thus, studies of brain activity and connectivity suggest that individuals who are more enmeshed with digital media also have a harder time (or need to devote more effort) initiating and sustaining self-regulatory processes, and may not be as facile at controlling responses to appetitive and potentially rewarding cues.

Evidence from EEG studies conducted on groups of heavy and addicted digital media users provides further support for the involvement of self-regulatory control mechanisms in these behaviors. Early work demonstrated that deficient executive abilities (assessed behaviorally) found in heavy internet users were paralleled by differences in the evoked potentials produced during a “Go/NoGo” response inhibition task (Dong et al., Reference Dong, Zhou and Zhao2011). The specific pattern exhibited by the heavy-use group reflected a relatively lower amplitude N2 (a frontally generated electrical potential that the authors associated with the conflict monitoring process that triggers the need to engage control), followed by a higher amplitude and delayed latency P3 (an evoked potential often associated with attention and response control). The authors interpreted these findings as evidence that the addicted group was less efficient at engaging control mechanisms during the task. Some subsequent work on internet use has replicated the reduced N2 potential during inhibitory control (Chen et al., Reference Chen, Liang, Mai, Zhong and Qu2016), while recent studies in excessive social media network users (Q. Gao et al., Reference Gao, Jia, Zhao and Zhang2019) and problematic smartphone users (L. Gao et al., Reference Gao, Zhang, Xie, Nie, Zhao and Zhou2020) point to a reversed pattern in which the more digitally engaged groups were found to evince a higher amplitude N2, and weaker P3 (specifically in the smartphone group) when trying to inhibit impulsive responses. While it can be challenging, even for experts in the field, to interpret the meaning of these differentiated components in the electrophysiological record, they do provide yet another source of evidence connecting digital media habits with the mechanisms underlying control.

So, though far from conclusive and based exclusively on correlational observations, the evidence seems to be broadly consistent with the notion that relative weaknesses in the brain systems supporting control may act as a gateway to digital media habit formation, and that diminished control could be a downstream consequence of prolonged or intensive periods of digital media involvement. However, some variation in the particular sites that emerge as significant across studies, and the occasionally reversed directionality of the findings (e.g., increases vs. decreases in regional volume, activity, connectivity, or evoked potentials), certainly warrant further consideration. One plausible explanation is that this variability is the result of unique brain-behavior relationships that exist for the diverse digital media experiences covered in this work. While the findings most consistently implicate key anterior and frontal nodes of the FP and CO networks, there is also considerably less evidence pointing to the involvement of parietal subregions of the FP and DAT networks (cf. Kei et al., Reference Kei, Naoya and Sayaka2020). This might indicate that digital media use is more closely tied to frontally mediated aspects of control – such as the establishment and maintenance of goal-state representations, and less connected to the parietal processes that dictate the shifting and orientation of attention (Chein & Schneider, Reference Chein and Schneider2005).

Digital Media and the Brain’s Reward Circuitry

A somewhat different perspective stems from the belief that digital media habits are connected to approach motivational and reinforcement processes. Under this view, the appetitive and rewarding features of digital media technologies – often embedded intentionally into digital platforms by their developers in order to stimulate more intense usage habits (Harris, Reference Harris2016) – might drive increased engagement with these platforms, disrupt the normal development of reward circuits, and at the extremes, give rise to maladaptive and addiction-like behaviors. Indeed, much of the work on digital media use draws upon the language and theories of addiction and reward dysregulation, and such diagnostic labels as internet-use disorder, internet addiction, internet gaming disorder, social network use disorder, and smartphone addiction are commonly applied in the literature to groups and individuals who exhibit seemingly excessive, problematic, or dependent use habits (Griffiths et al., Reference Griffiths, Kuss, Demetrovics, Rosenberg and Curtiss Feder2014; Gutiérrez et al., Reference Gutiérrez, de Fonseca and Rubio2016; Kirby et al., Reference Kirby, Dapore, Ash, Malley and West2020; Yao et al., Reference Yao, Liu and Ma2017).

As with claims regarding control, theories linking aberrant reward processing and responsivity to digital media habits also gain some purchase in correlational behavioral evidence. Across several studies exploring the behavioral and trait correlates of different digital media modalities, there is compelling evidence that individuals who tend to be more engaged with these media also tend to exhibit greater general reward sensitivity and responsivity (Sanbonmatsu et al., Reference Sanbonmatsu, Strayer, Medeiros-Ward and Watson2013) – particularly with respect to more immediate rewards (Hadar et al., Reference Hadar, Hadas, Lazarovits, Alyagon, Eliraz and Zangen2017; Tang et al., Reference Tang, Zhang, Yan and Qu2017; Wilmer & Chein, Reference Wilmer and Chein2016), and have greater difficulty with reward reinforcement learning (Meshi et al., Reference Meshi, Elizarova, Bender and Verdejo-Garcia2019).

If the development of reward-relevant processes plays a role in the formation of digital media habits, or results in reward dysregulation that causes greater vulnerability to relevant problematic outcomes, then we might expect to observe such effects within the brain’s reward circuitry (see Figure 5.1). This circuitry includes the dopaminergic pathways that connect the ventral tegmental area of the brainstem (where dopamine is produced) to the nucleus accumbens of the VS, the amygdala, and the ventromedial prefrontal cortex (vmPFC) – especially its ventral-most extent comprising the orbitofrontal cortex (OFC), a region linked to aberrant reward processes in patients with other substance-related and behavioral addictions (Kuss et al., Reference Kuss, Pontes and Griffiths2018). From a developmental perspective, these reward-processing structures are known to undergo a rapid period of change around the onset of puberty, which is thought to explain why adolescence constitutes a period of particularly heightened reward responsivity (Blakemore & Robbins, Reference Blakemore and Robbins2012; Sisk & Zehr, Reference Shulman, Smith and Silva2005; Spear, Reference Spear2010).

Neuroimaging work suggests that the dysregulation of reward-relevant regions, especially the OFC, VS, and amygdala, may indeed be a hallmark for the addiction-like behaviors found in association with a range of digital media forms (Kuss et al., Reference Kuss, Pontes and Griffiths2018; Lin & Lei, Reference Lin, Lei, Montag and Reuter2015; Turel et al., Reference Turel, He, Xue, Xiao and Bechara2014). A study on addicted players of the online video game World of Warcraft (Ko et al., Reference Ko, Liu and Hsiao2009) was among the first to show this relationship. Specifically, the study found that excessive gamers, relative to a comparison group of game novices, evinced increased activity not only in self-regulatory processing regions (dlPFC, dmPFC), but also in the OFC and VS, when presented with game-related cues that aroused the urge to play. Several subsequent studies examining brain structure in participants engaged in especially high levels of internet and smartphone use have found corroborating evidence of gray matter abnormalities in the OFC (Hong et al., Reference Hong, Kim and Choi2013; Lee et al., Reference Lee, Namkoong, Lee, Lee and Jung2019; Lin & Lei, Reference Lin, Lei, Montag and Reuter2015; Zhou et al., Reference Zhou, Montag and Sariyska2019).

Studies examining the number of social relationships that one forms by way of online social networking sites also point to the relevance of reward processing centers in the brain. Building on prior work examining the neural correlates of both online and offline social network size (Bickart et al., Reference Bickart, Wright, Dautoff, Dickerson and Barrett2011; Kanai et al., Reference Kanai, Bahrami, Roylance and Rees2012), Von der Heide and colleagues demonstrated that having a larger online social network, as measured by participants’ actual number of Facebook friends, was associated with greater gray matter volume in multiple reward-relevant brain regions, including the bilateral amygdala and OFC (Von der Heide et al., Reference Von der Heide, Vyas and Olson2013). A related study on the structural brain correlates of actual Facebook use – this time indexed by participants’ mobile device Facebook use over a five-week period (Montag et al., Reference Montag, Markowetz and Blaszkiewicz2017) – found that higher frequency and duration of mobile Facebook use were both associated with decreased gray matter volume of the bilateral VS. Other related work has found evidence of decreased gray matter volume in the bilateral amygdalae of those reporting generally heavier use of social networking sites (i.e., not focused on a particular platform; W. He et al., Reference He, Turel and Bechara2017). As we noted earlier, relative increases and decreases in the volume of regional gray matter can be difficult to interpret in functional terms, but such findings, at the very least, suggest that there are relevant linkages between digital media habits and the processes enacted within these reward-processing centers.

Here again, we can turn to fMRI studies involving task-based manipulations of the digital media environment to corroborate and clarify the structural findings. In one early neuroimaging study on social media behavior (Turel et al., Reference Turel, He, Xue, Xiao and Bechara2014), heavy Facebook users were scanned while performing a task that required them to respond to Facebook-relevant cues (iconography taken from the Facebook platform) while withholding responses to irrelevant cues (traffic signs), or vice versa. Among the regions tested, only one exhibited a pattern of activity that predicted individual differences in Facebook addiction – the VS. That is, the level of one’s Facebook addiction selectively related to how strongly this central reward value processing center responded in association with Facebook images. The importance of the VS in social media behaviors was similarly underscored in another early study of Facebook users in which social feedback given to participants was experimentally manipulated in a simulated social media environment (Meshi et al., Reference Meshi, Morawetz and Heekeren2013). In this study, the authors found that actual Facebook usage was associated with how active the VS became when participants received positive social feedback for themselves (compared to others) in the simulated platform. Another widely cited neuroimaging study deployed a simulated version of the Instagram social networking platform (Sherman et al., Reference Sherman, Payton, Hernandez, Greenfield and Dapretto2016). In the study, high school students submitted photos from their own actual Instagram accounts and were told that their photos, along with photos provided by others, would be viewed by the participants in the study, and either liked or not liked; in reality, the number of likes and the content (neutral or risky behaviors) of the photos were manipulated by the researchers as part of their experimental design. FMRI evidence showed increased activation in the VS, along with several other regions, when participants saw that their own images had received a higher number of likes, and also when viewing neutral photos that were more liked by others. These findings suggest that receiving positive social feedback via social media, and evaluating the relative social value of the information (e.g., photos) others post on these platforms, engages the same brain processes that generally signal rewarding experiences. A related study conducted in adolescents (Cascio et al., Reference Cascio, O’Donnell, Bayer, Tinney and Falk2015) investigated whether these same processes might influence online decisions about whether or not to conform with others’ expressed preferences. During a scanning session, subjects were shown the recommendations (ratings) that they and others had given for a set of smartphone apps, and were then given the chance to revise their own prior rating. Analyses indicated greater activation in both the VS and OFC when participants changed, rather than maintained, their initial rating, which suggests once again that reward valuation signals play a role in dictating this facet of online behavior. Indeed, two recent companion studies exploring the neural processes underlying the selection and sharing of digital media content likewise implicate this same reward valuation network (Baek et al., Reference Baek, Scholz, O’Donnell and Falk2017; Scholz et al., Reference Scholz, Baek, O’Donnell, Kim, Cappella and Falk2017). Specifically, these studies found that the VS and OFC were among the most strongly engaged regions when participants opted to share news headlines via social media in a simulated task, and in association with headlines that are actually the most “viral” (i.e., shared in real-life media) at the population level.

Functional and structural connectivity approaches provide still further evidence of reward circuitry involvement in mediating the nature and intensity of one’s digital media habits, though the directionality of these findings is somewhat nuanced. While some studies suggest that heavier digital media involvement is tied to disrupted (weaker) integration among the brain regions that process reward-relevant information (e.g., functional connectivity with the VS is reduced in internet addicts; Zhang et al., Reference Melchers, Li, Chen, Zhang and Montag2015), other studies find that heavier digital media use is associated with stronger interconnectivity among reward regions (e.g., the integrity of white matter pathways connecting the VS and OFC is stronger in heavy smartphone users [Wilmer et al., Reference Wilmer, Hampton, Olino, Olson and Chein2019]; functional connectivity of the amygdala to other regions is a correlate of adolescent smartphone dependence [Tymofiyeva et al., Reference Tymofiyeva, Yuan and Kidambi2020]).

EEG studies examining online gaming and smartphone addiction also lend support to the idea that heavy digital media use is associated with altered neural activity in the reward system. Relevant studies on internet behavior have found, for instance, that online gaming addicts produce an attenuated P300 in response to receiving rewards (Duven et al., Reference Duven, Müller, Beutel and Wölfling2015), and that individuals who report excessive internet use evince both a smaller feedback-related negativity in response to reward gains, and a larger P300 in response to losses (W. He et al., Reference He, Turel and Bechara2017), which could indicate stronger reinforcement sensitivity and weaker punishment sensitivity, respectively. Recent work on smartphone habits similarly observed an altered reward positivity potential among heavier smartphone users, but no association between intensity of use and the amplitude of the parietal P3 (which the authors considered an index of higher-level decision processing), leading to the conclusion that smartphone addiction may be selectively correlated with reward processing, and not higher-level deliberative processes (Kirby et al., Reference Kirby, Dapore, Ash, Malley and West2020).

Overall, the behavioral and imaging findings connect reward-related brain systems to a range of digital media behaviors. This work spans early forms of digital media, including video gaming and internet use habits, but also harnesses one of digital media’s currently most widespread and time-consuming manifestations – social media networking. While some studies find that digital media behaviors are selectively associated with reward-related signals and locations in the brain (e.g., Kirby et al., Reference Kirby, Dapore, Ash, Malley and West2020), many of the relevant studies also contain evidence for the involvement of regions thought to undergird other, more disparate, functions (e.g., Ko et al., Reference Ko, Liu and Hsiao2009; Sherman et al., Reference Sherman, Payton, Hernandez, Greenfield and Dapretto2016). Indeed, as we consider in greater depth in the next section, several studies highlighting the relevance of reward circuitry in digital media habits (e.g., Cascio et al., Reference Cascio, O’Donnell, Bayer, Tinney and Falk2015; Horvath et al., Reference Horvath, Mundinger and Schmitgen2020; Sherman et al., Reference Sherman, Payton, Hernandez, Greenfield and Dapretto2016) also indicate the relevance of regions more typically associated with social information processes (rather than reward processes, per se).

Digital Media and the Brain’s Social Processing Systems

Increasingly, many of our day-to-day social interactions take place on digital platforms, and it has been argued that social networking sites now serve as an independent medium for developing and maintaining social connectedness despite being devoid of direct face-to-face interactions (Grieve et al., Reference Grieve, Indian, Witteveen, Anne Tolan and Marrington2013; Spies Shapiro & Margolin, Reference Spies Shapiro and Margolin2014). As such, researchers have leveraged the quantification of social interactions supported by online social networking sites like Facebook and Instagram to explore whether, and how, this type of digital social context relates to psychological and brain functioning.

The strength and directionality of the influence of digital screen engagement and social media networking on psychosocial functioning in developmental populations is a subject of significant debate (Coyne et al., Reference Coyne, Rogers, Zurcher, Stockdale and Booth2020; Przybylski et al., Reference Przybylski, Orben and Weinstein2020; Twenge et al., Reference Twenge, Haidt, Joiner and Campbell2020), and evidence from both longitudinal and large-scale secondary data analysis suggests that the relationship is likely smaller and more nuanced than has sometimes been claimed (Coyne et al., Reference Coyne, Rogers, Zurcher, Stockdale and Booth2020; Przybylski et al., Reference Przybylski, Orben and Weinstein2020). Any impact of social media behaviors is also likely to vary across different social networking platforms. For example, some studies in adolescent and young adult samples find that heavier use of Facebook, but not YouTube or Twitter, is related to higher self-reported levels of social connectedness (Alloway et al., Reference Alloway, Horton, Alloway and Dawson2013; Alloway & Alloway, Reference Alloway and Alloway2012), potentially due to the built-in features in Facebook that facilitate more sharing of personal content.

The expectation that aspects of digital media involvement could be motivated by, or have an impact on, social exchange, has led some investigators to pursue evidence of associations between the brain’s social information processing networks (see Figure 5.1) and digital media habits. There is, however, only partial consensus regarding which specific brain regions participate selectively in social information processes. The brain regions most consistently implicated in social information processing are the temporoparietal junction and neighboring (posterior) superior temporal sulcus along with the midline (ventromedial) prefrontal cortex. Some treatments of the “social brain” also variably include the precuneus and adjacent posterior cingulate cortex, and the anterior temporal poles (Adolphs, Reference Adolphs2009; Becht et al., Reference Becht, Wierenga and Mills2021; Mills et al., Reference Mills, Lalonde, Clasen, Giedd and Blakemore2014). Together, these regions are thought to support empathy, mentalizing, social perspective taking, and the processing of social feedback. The conspicuous proximity of the medial prefrontal areas implicated in social feedback processing and those associated with the valuation of primary rewards, such as food and sex (Bartra et al., Reference Bartra, McGuire and Kable2013; Lieberman & Eisenberger, Reference Lieberman and Eisenberger2009) has led some researchers to emphasize the functional overlap between these systems (Bhanji & Delgado, Reference Bhanji and Delgado2014; Braams et al., Reference Braams, Peters, Peper, Güroğlu and Crone2014), as have studies showing engagement of mesolimbic “reward” regions in putatively “social” tasks. Likewise, colocalization of regions implicated in social information processing tasks with areas that also exhibit increased engagement when our minds are supposed to be at rest – comprising a so-called default mode network – has spurred additional theorizing on the specific operations that are supported by these brain areas (Mars et al., Reference Mars, Neubert, Noonan, Sallet, Toni and Rushworth2012).

Work exploring the links between the magnitude of one’s online social network size and brain structure points not only to the involvement of nodes in the brain’s reward system, as was noted earlier, but also to the involvement of social processing areas such as the temporoparietal junction (Kanai et al., Reference Kanai, Bahrami, Roylance and Rees2012). The involvement of the social brain in digital media experiences is further suggested by work examining the association between intrinsic functional organization in the brain and individual differences in the sharing of personal (self-related) information on Facebook (Meshi et al., Reference Meshi, Mamerow, Kirilina, Morawetz, Margulies and Heekeren2016). Specifically, analyses focused on how social processing centers in the vmPFC and precuneus connect up with the rest of the brain. The study revealed that the strength of connectivity between these regions and the lateral PFC predicted a greater tendency to share information with others on Facebook, while stronger connectivity between the precuneus and ATP predicted less sharing. Such findings suggest that the decision to broadcast personally relevant information via social media may depend in part on how one mentally represents social relationships (interactions between the self and others) in these regions.

Other recent work has explicitly investigated whether the structure of social brain regions might also explain the overall amount of time one spends on social media (Turel et al., Reference Turel, He, Brevers and Bechara2018). Based on behavioral findings establishing that the effort to maintain and navigate online social relationships is subjectively perceived as demanding (Turel et al., Reference Turel, He, Brevers and Bechara2018, Exp. 1), the authors wondered whether the social skills used to keep up with these demands might also be reflected in the neuroanatomical correlates of social information processing. Seeming to confirming this hypothesis, structural MRI analyses revealed a significant positive correlation between overall Facebook usage and the gray matter volume of a superior temporal site near the temporoparietal junction (Turel et al., Reference Turel, He, Brevers and Bechara2018, Exp. 2). That is, those with more gray matter in a temporal region of the social brain network reported spending more time on Facebook, which the authors thought could reflect the relative sophistication of the social skills that they rely on to maintain extended online social networks, or their relative adeptness at deploying these skills.

The idea that possessing stronger social skills might promote greater digital media involvement is, however, in an interesting juxtaposition with findings suggesting that individuals with lower social empathy also tend to be more enmeshed with certain digital media (Decety & Lamm, Reference Decety and Lamm2006; Engelberg & Sjöberg, Reference Engelberg and Sjöberg2004; Melchers et al., Reference Melchers, Li, Chen, Zhang and Montag2015). As such, while heavier media use might coincide with a stronger ability to understand others’ perspectives (i.e., advanced social processing skills), it may also coincide with less actual concern for others’ emotional states. Evidence from EEG studies aimed at investigating the neural basis of empathic processing among individuals who report heavy internet use reinforces this speculation. Specifically, multiple studies have found that participants with high internet addiction scores (compared to healthy controls) exhibit an undifferentiated electrical response when viewing images of others in painful versus nonpainful circumstances, whereas the EEG record in healthy controls shows discrimination of these conditions (Jiao et al., Reference Jiao, Wang, Peng and Cui2017; T. Wang et al., Reference Wang, Ge, Zhang, Liu and Luo2014). That is, internet-addicted individuals exhibit (at least in their EEGs) a relative absence of empathy for others’ discomfort.

The evidence considered above demonstrates that some digital media experiences are associated with brain regions and patterns implicated in social information processing. Namely, they show that key nodes in the social brain, including the temporoparietal junction, the precuneus, and the vmPFC, are likely to play a role in determining how digital media users navigate through the complexities of online social networking space and how they form representations of others’ perspectives and feelings. We note, however, that the supportive findings in this domain may be less abundant in the literature than those implicating self-regulatory control and reward-relevant processes. This state of affairs could indicate the differential contributions of these systems to digital media behaviors, or could simply reflect the fact that few studies have thus far deliberately tried to disentangle the social facets of digital media interactions from its inherent rewards and high-level processing demands. We anticipate that this will be a focal aim of future work in this space.

Digital Media Use in the Developing Brain

Above, we harnessed evidence from the young adult literature in order to establish the involvement of control, reward, and social brain systems in digital media experiences. Guided by this evidence, and with some knowledge of the expected trajectory of development within these systems, we turn now to a limited body of developmental neuroscientific work that might help us to understand how these brain–behavior relationships manifest in earlier stages of development: to discern whether the patterns observed in young adulthood are already present in earlier life, whether brain–behavior relationships emerge specifically in conjunction with the maturation of the three systems (or with other brain systems), or whether the patterns discussed above are only characteristic of later stages of development.

Studies in Early Life and Childhood

While early childhood screen and digital media exposure is a widely researched topic, only a handful of studies have deployed noninvasive brain imaging methods in the effort to illuminate potential interrelationships with brain development. Perhaps the earliest developmental glimpse comes from MRI and EEG studies conducted in preschoolers (Hutton et al., Reference Hutton, Dudley, Horowitz-Kraus, Dewitt and Holland2020; Zivan et al., Reference Zivan, Bar, Jing, Hutton, Farah and Horowitz-Kraus2019). One MRI-based diffusion tractography study (Hutton et al., Reference Hutton, Dudley, Horowitz-Kraus, Dewitt and Holland2020) found that, already by preschool (ages 3–5), screen time exposure is associated with widespread reductions in white matter integrity, a sign that these children have less well-developed structural connections between brain regions. While several tracts (pathways) exhibited this association, those associated with executive function, multimodal association, visual processing, and language were especially implicated. The same research group also conducted an EEG study of children aged 4–6 (Zivan et al., Reference Zivan, Bar, Jing, Hutton, Farah and Horowitz-Kraus2019), which found that six weeks of exposure to screen-based, digitally recorded, stories, compared to live human storytelling, resulted in weaker attentional gains and a resting-state EEG pattern characteristic of attentional disruption (increased theta/beta power ratio). Meanwhile, a study exploring screen-based media habits in a group of older children (aged 8–12; Horowitz-Kraus & Hutton, Reference Horowitz-Kraus and Hutton2018) indicated that increased media exposure might be associated with decreased resting-state connectivity between both cognitive control and language regions of the brain and the visual word form area, a region known to be important in the acquisition and execution of reading skills. The authors speculated that this pattern might have arisen because substantial screen time disrupts the normal development of the regions that support reading skill in the brain. Recently, Horowitz-Kraus and colleagues (Reference Horowitz-Kraus, DiFrancesco and Greenwood2020) followed up on this discovery to investigate whether functional connectivity patterns in this age range might also relate to the ratio of time that children spend in front of screens versus reading; this time considering these relationships for both typical readers and children with reading difficulties. The two reading ability groups exhibited similar screen-to-reading time ratios but, selectively for the children with reading difficulties, a relatively greater proportion of screen time activity was related to increased functional connectivity in the salience and executive control networks. The authors suggested that this pattern might reflect inefficient engagement of control processes when reading (and presumably when engaging in other cognitively challenging tasks), which might ultimately lead these children to greater screen dependency (though see Y. Ophir et al., Reference Ophir, Tikochinski and Rosenberg2020). Through we still only have correlational evidence from these studies, the findings are at least consistent with the idea that screen time exposure, particularly during earlier stages of dynamic brain growth and development, might be intertwined with the processes supporting self-regulatory control, especially among those with existing developmental deficits.

Paulus and colleagues (Reference Paulus, Squeglia and Bagot2019) recently reported findings from the first large-scale investigation aimed at relating screen media activity to structural brain characteristics in prepubescent youth (ages 9 and 10 at recruitment), using the structural imaging and survey data from a 4,277-participant subset of the first cross-sectional release of the Adolescent Brain Cognitive Development (ABCD) study (Volkow et al., Reference Volkow, Koob and Croyle2018). The authors characterized and quantified screen media activity via multivariate analyses of survey responses provided by parents and youth. Overall, these analyses produced significant but complex patterns of relationship between structural brain indices (cortical thickness, sulcal depth, and gray matter volume) and screen media activity. In particular, the factor accounting for the most variance in screen media activity showed that greater screen involvement was linked to widespread cortical thinning and gray matter volume reductions (along with greater levels of externalizing psychopathology and lower crystallized intelligence). Interestingly, this pattern held for regions supporting both early sensory processing and higher order functions. However, the specific pattern of relationship was also found to depend on the type of screen media behavior (e.g., social media vs. gaming) – for instance, greater exposure to gaming-related activities was associated with thinner cortex, but also larger regional volume (e.g., OFC) and higher crystallized intelligence. Moreover, other latent factors capturing variance in the screen media activity data suggested disparate patterns of relationship between screen activity and brain structure. In light of this diversity of findings, the authors cautioned that screen media activity cannot be reduced to being simply “good” or “bad” for brain structure and function.

Studies in Adolescents

There is a widely observed rise in digital media involvement during adolescence. With adolescence, pubertal processes advance the brain into a period characterized by rapid change in both the midline dopaminergic reward system and in the extended network of brain regions involved in social information processing (Blakemore, Reference Blakemore2008, Reference Blakemore2012). It is, accordingly, tempting to speculate that the changes taking place in these brain systems might explain the escalation of digital media use during this period, and earlier in this chapter we presented some relevant and corroborative findings (e.g., Cascio et al., Reference Cascio, O’Donnell, Bayer, Tinney and Falk2015; Sherman et al., Reference Sherman, Payton, Hernandez, Greenfield and Dapretto2016; Tymofiyeva et al., Reference Tymofiyeva, Yuan and Kidambi2020; Yuan et al., Reference Yuan, Qin and Wang2011). Unfortunately, much of the additional literature on brain-to-digital-media relationships during “adolescence” has involved either very late adolescent cohorts, or participant samples spanning a wide age range that may include some younger adolescents but also extends into young adulthood (i.e., participants in their early to mid- twenties; F. Lin et al., Reference Lin, Zhou and Du2012; Moisala et al., Reference Moisala, Salmela and Hietajärvi2016, Reference Moisala, Salmela and Hietajärvi2017; Von der Heide et al., Reference Von der Heide, Vyas and Olson2013; H. Wang et al., Reference Wang, Jin and Yuan2015). Thus, it can be difficult to draw meaningful developmental conclusions from this corpus of work.

The few studies using somewhat more constrained age cohorts (i.e., including only adolescents aged 18 or under) produce intriguing, but varied outcomes. One study, for instance, observed that internet gaming habits among a group of 14- to 17-year-olds related to disrupted blood flow patterns (as measured by MRI-based arterial spin labeling) in a large number of brain areas, including some linked to reward-relevant processing (e.g., amygdala) (Feng et al., Reference Feng, Chen and Sun2013). Using functional connectivity methods, another study found that a group of gaming addicts, aged 12–17, also evince relatively increased connectivity between the posterior cingulate cortex and several other social- and reward-relevant regions, including the precuneus and the nucleus accumbens (Ding et al., Reference Ding, Sun and Sun2013). However, more recent work (Chun et al., Reference Chun, Choi and Cho2018) on excessive smartphone use among adolescents aged 12–18 found that smartphone usage intensity related to significantly weaker intrinsic resting-state connectivity within the reward network (OFC to VS) and between the control and reward systems (OFC to mACC), with weakened OFC–VS functional connectivity also found to be predictive of the severity of smartphone withdrawal symptoms reported by the group. Thus, evidence on the relationship between digital media behaviors and functional connectivity across regions of the reward and social processing networks appears to be nuanced, and difficult to align neatly with specific theories of development.

Age Group Comparisons and Longitudinal Studies of Youth

Cross-sectional evidence comparing digital media use among different age cohorts could help us to determine whether observed brain–behavior relationships simply track with the trajectory of normative brain development, or rather, contain evidence for a causative effect of digital media behavior on brain development. Unfortunately, the literature is almost completely lacking studies that directly compare one age group to another. The only notable exception is Sherman et al.’s (Reference Sherman, Greenfield, Hernandez and Dapretto2018) replication and extension of their earlier work in adolescents (Sherman et al., Reference Sherman, Payton, Hernandez, Greenfield and Dapretto2016), in which they leveraged the same simulated Instagram paradigm to collect comparison data from an older young adult cohort comprised of university students (Sherman et al., Reference Sherman, Greenfield, Hernandez and Dapretto2018). Similar to their prior findings in adolescents, the young adults evinced greater activation in social- and reward-relevant brain regions, including the precuneus, vmPFC, and VS, when viewing images from their own Instagram accounts that had received more versus fewer likes. Indeed, the direct contrast between adolescents’ and young adults’ brain activity when these groups received social feedback on their own images produced no significant differences, other than a small region of the visual cortex. This congruency across the two age cohorts suggests that adolescents’ elevated concerns toward “popularity” likely persist into young adulthood. That is, the sensitivity of the brain’s social and reward circuitry might rise with adolescence, but then plateau in young adulthood. When viewing others’ images, however, important age differences did emerge in control-relevant regions of the brain. Namely, while the adolescent sample had exhibited diminished engagement of control regions when viewing risky compared to non-risky/neutral images, young adults showed equivalent activity in the two conditions. In other words, the young adults responded to the images of risky activities by activating the self-regulatory control regions that inhibit actual involvement in such behaviors, while the adolescents did not appear to do so. Indeed, a direct contrast across the two age groups indicated significantly greater activation for young adults in both the dmPFC and dlPFC when viewing risky images. Together, these findings accord with a dual systems framework (Shulman et al., 2016; Steinberg, Reference Steinberg2008), wherein the sensitivity of reward circuitry levels off as the brain’s control and attention systems reach young adult maturity, and show that changes in these interacting systems likely hold relevance for developing digital media habits.

Longitudinal examinations of brain structure and function spanning different stages of development could be especially fertile territory for furthering our understanding of the origins and effects of digital media use. Though relevant longitudinal studies are currently underway (e.g., ABCD; Volkow et al., Reference Volkow, Koob and Croyle2018), the findings available to date generally come from relatively short-term longitudinal investigations that are not specifically informative with respect to development. In one study, for instance, a six-week internet gaming exposure enacted with naïve and experienced young adult gamers resulted in short-term longitudinal reductions in left OFC volume (Zhou et al., Reference Zhou, Montag and Sariyska2019), which could be interpreted as evidence that video game play affects one of the important centers for reward processing. Another short-term intervention study found that when internet-naïve adults were given four weeks of increased internet access, they started to exhibit higher rates of media-multitasking, but there were no significant changes detected in brain structure (Loh et al., Reference Loh, Chakraborty and Sadhu2019). A more extended longitudinal undertaking involved a three-year study conducted in a large sample of Japanese children and adolescents (aged 5–18) aimed at exploring how various digital media behaviors (TV viewing, video gaming, internet use) might prospectively impact brain development (Takeuchi et al., Reference Takeuchi, Taki and Hashizume2015, Reference Takeuchi, Taki and Hashizume2016, Reference Takeuchi, Taki and Asano2018). While the basic prospective longitudinal approach represents the type of method that could inform our understanding of digital media’s causal impacts on brain development, the outcomes are quite challenging to put into a coherent narrative. Notably, the work assessed digital media behaviors only at the start of the study, with no follow-up assessment of how habits may have changed over the longitudinal period. There was also no consistency or specificity in the findings with respect to the particular brain areas whose longitudinal change was predicted by baseline digital media habits, and disparate MRI modalities (gray/white matter volume, mean diffusivity of diffusion MRI) were needed to obtain significant brain–behavior relationships across media types. Most important, there was no reported attempt to delineate specific developmental patterns, despite the longitudinal nature of the data and the wide age range of the participants at entry to the study. Finally, as was alluded to by the authors themselves, the cohort project began in 2008, which predates the widespread availability and popularity of smartphones, social media, and online games in Japan. This observation underscores how work of this nature may be subject to cohort effects introduced by the ever-changing technology climate.

Conclusions: What We Know Now and Where We May Be Headed

Alongside rapid advancements in digital technology, recent years have witnessed a growing body of work dedicated to understanding the potential impact of digital media behaviors on psychological and brain functions. In this chapter, we reviewed a growing literature deploying various MR imaging and complementary electrophysiological methods that might inform our understanding of the links between brain development and digital media behaviors. Broadly, we sought to examine whether the data accord with current perspectives on digital media involvement that emphasize maturing self-regulatory control skills, a heightened sensitivity to rewards, and shifts in responsivity to socially relevant inputs. Acknowledging important limitations in the available developmental evidence, we first considered how well these perspectives address the body of data obtained primarily from young adult populations, and then surveyed the findings from earlier life for evidence that might provide traction in clarifying the developmental origins of observed brain–behavior relationships.

Overall, there is corroborative evidence denoting each of the three highlighted systems (control, reward, social). That is, for each perspective, there appear to be an ample number of supportive findings from across different types of digital media (e.g., internet behaviors, smartphone use, social media involvement, media-multitasking, etc.) and from multiple neuroinvestigative modalities (various MRI-based approaches, EEG). There are, likewise, some examples from research conducted in younger developmental samples pointing to digital media interactions with some of the same neural substrates of control, reward, and social processes that are featured in the young adult literature.

However, we also come across findings that compel more nuanced accounting of the relationships between digital media involvement and brain development. First, across studies, modalities, and age groups, even the most affirming observations – that is, those implicating expected neural correlates of control, reward, or social processing – place differential emphasis on separate regions/subcomponents within a given brain system, and moreover, at times appear to indicate opposing directional patterns (e.g., increases vs. decreases in regional volume/activity/connectivity, positive vs. negative correlations with digital media involvement, stronger vs. weaker engagement across development). These differences may just be the consequence of noisy measurement approaches (e.g., in the characterization of digital media behaviors or the indexing of brain structure/function), but could also reflect actual, and potentially meaningful, differences in the brain–behavior relationships that exist for certain digital media experiences and particular populations. The outcomes may depend, for example, on whether one is examining the addiction-like or excessive digital media behaviors that are emphasized in the disease-oriented approach that dominates much of the field, or whether one is examining more normative day-to-day patterns of engagement with digital media technologies.

We should also be mindful of some specific limitations in how we have approached this review. First, while we present the findings as though each of the three emphasized systems (control, reward, social) can be considered independently, this assumption is plainly fraught, not only because there is imperfect agreement about which specific regions contribute to each system as well as some neuroanatomical overlap between them (e.g., medial PFC, parietal cortex), but more importantly, because the real story of digital media’s relationship with brain development almost certainly lies in the complex and dynamic interactions that take place between these systems, and in how these interactions shift over the course of development. Second, this approach to review reflects a form of confirmation bias. That is, with the expectation that the brain areas associated with control, reward, or social information processing might be relevant to the link between brain development and digital media behaviors, we sought out examples in the literature that could affirm this expectation, while being less attentive to evidence that could potentially lead us toward a different, perhaps overlooked, explanation. By way of example, though we proffered the work conducted by Horvath et al. (Reference Horvath, Mundinger and Schmitgen2020), Sherman et al. (Reference Sherman, Payton, Hernandez, Greenfield and Dapretto2016), and Turel et al. (Reference Turel, He, Brevers and Bechara2018) as examples implicating control, reward, and social mechanisms, respectively, each of these studies also reported significant findings in the medial temporal lobe (the hippocampus or neighboring cortices), which might encourage us to consider the relevance of episodic memory mechanisms enacted within the medial temporal lobe in the relationship between digital media habits and brain development. Likewise, our review could have devoted greater attention to emerging evidence of digital-media-dependent effects on primary visual and somatosensory cortices, and the possibility that daily intensive digital media use is leading to the plastic reshaping of these cortical areas (Gindrat et al., Reference Gindrat, Chytiris, Balerna, Rouiller and Ghosh2015).

Despite the negative attitudes toward digital media involvement often emphasized in public outlets (Bennett, Reference Bennett2017; Parks, Reference Parks2020), the causal impacts of digital media habits on the developing brain remain unclear, due in part to the relative absence of longitudinal work and largely correlational nature of cross-sectional studies, and to the challenges that naturally arise with neuroscientific work conducted with younger populations. Emerging technologies beyond fMRI and EEG could be helpful in circumventing some of these practical limitations. For instance, functional near-infrared spectroscopy, a wearable and relatively low-cost tool that is used across a wide range of populations from preterm infants to the elderly (Pinti et al., Reference Pinti, Tachtsidis and Hamilton2020; Rahimpour et al., Reference Rahimpour, Noubari and Kazemian2018) could be fruitfully applied to examine the brain correlates of digital media habits as they arise in real-world settings. Meanwhile, noninvasive brain stimulation methods could help us to close the causal chain by revealing how experimentally induced alteration of brain states affects digital media behaviors. Findings demonstrative of behavioral change following brain stimulation in other relevant contexts, such as inhibitory control (Cai et al., Reference Cai, Li and Liu2016; Stramaccia et al., Reference Stramaccia, Penolazzi, Sartori, Braga, Mondini and Galfano2015) and risk taking (Figner et al., Reference Figner, Mackinlay, Wilkening and Weber2009; Gilmore et al., Reference Gilmore, Dickmann, Nelson, Lamberty and Lim2018), suggest that it may even be possible to use brain stimulation technologies to alter the course of digital media habit formation or to ameliorate impacts on other behaviors (Hadar et al., Reference Hadar, Hadas, Lazarovits, Alyagon, Eliraz and Zangen2017).

So, where does this leave us? To put it plainly, despite a now sizable literature on associations between the brain and digital media behavior, it is clear that there is much still to be learned. Within an ever-changing media technology landscape, it has proven challenging to address the essential questions that motivate work in the field. Are there specific brain markers present during the course of development that can reliably predict subsequent digital media habits, or that might signal greater susceptibility to any harmful outcomes of these habits? Is brain development influenced in any particularly meaningful way by earlier, or more extended, exposure to digital media technologies? As much as we would like to forward conclusive answers to these questions, the only answer we can justifiably offer as a field is that we do not yet know. But, armed with the many valuable insights provided by the extant literature, and with clarifying evidence that will most certainly emerge through longitudinal and convergent methodology studies on the near horizon, we are optimistic that the field will continue to narrow the gaps in our understanding, and bring us closer to more edifying answers.

6 Adolescents’ Digital Media Interactions within the Context of Sexuality Development

Chelly Maes , Johanna M. F. van Oosten , and Laura Vandenbosch

Digital media interactions have become an integral part of adolescents’ everyday lives as a wide range of evolving technological tools (e.g., smartphones) allow adolescents to be online almost continually (Davis, Reference Davis2013). As such, the context in which teens mature has now expanded from the traditional offline context to the online environment (Lerner et al., Reference Lerner, Boyd, Du, Weiner and Craighead2010). One of the most significant developmental tasks, which is facilitated through the use of digital media, is the construction of one’s sexuality (Collins et al., Reference Collins, Martino and Shaw2010).

Within the current chapter, the uses of different digital media applications are discussed in the context of the establishment of a sexual identity. In particular, the chapter focuses on social media, sexting, and online pornography. The literature has explained that the unique affordances of these media (i.e., accessibility, anonymity, and asynchronous communication) invite adolescents to use them for the construction of a sexual identity (e.g., Valkenburg & Peter, Reference Valkenburg and Peter2011).

The current chapter situates adolescents’ sexually oriented digital media use by first describing adolescents’ sexuality development. Then, the chapter delves into (1) adolescents’ varying sexually oriented digital media activities, (2) motivators for these activities, and (3) outcomes of such uses with attention for potential underlying processes, and the possible conditional nature of such outcomes. The chapter concludes with recommendations for future research that should help to bolster our understanding of adolescents’ digital media interactions and their impact on sexuality.

Adolescent Sexuality Development

Adolescence marks a time of self-discovery and is characterized by profound physical, cognitive, psychological, and sociocultural changes (Sawyer et al., Reference Sawyer, Afifi and Bearinger2012). Within this unique developmental context, the exploration and construction of an adolescent’s sexuality is believed to be one of the most significant and challenging developmental tasks (Fortenberry, Reference Fortenberry, Bromberg and O’Donohue2013). In the literature, sexuality often denominates an inclusive category that refers to how adolescents describe, feel, or express their sexual selves (Diamond & Savin-Williams, Reference Diamond, Savin-Williams, Lerner and Steinberg2009).

Sexuality development has received growing attention over the past 40 years, with early studies responding to concerns of educators and parents regarding adolescents’ early sexual initiation or negative consequences of sexual activities, such as unwanted pregnancies (Moran, Reference Moran2000). In recent years, sexuality scholars have increasingly acknowledged adolescents’ emerging sexual feelings and behavioral responses as expected and thus developmentally normative without undermining the necessity of exploring sexual risks (e.g., Tolman & McLelland, Reference Tolman and McClelland2011). In this view, scholars point to the usefulness of studying how adolescents construct a “positive sexuality” (Russell, Reference Russell2005). Maes et al. (Reference Maes, Trekels, Impett and Vandenbosch2022), for instance, refer to a positive approach to sexual relationships, acceptance of one’s own sexuality, a respectful approach to different sexual expressions of others, the ability to have control over sexual interactions, and resilience against negative sexual experiences. Yet, most research still focuses on negative sexuality and thus addresses indicators such as sexual uncertainty, sexual objectification, and risky sexual behaviors (e.g., Peter & Valkenburg, Reference Peter and Valkenburg2009; Reference Peter and Valkenburg2011). In the current chapter, a focus will be placed on both positive and negative sexuality-related attitudes and behaviors.

Adolescents’ sexuality development is typically driven by elevated levels of sexual hormones (e.g., testosterone and estrogen levels) that increase sexual drives and stimulate the development of primary (i.e., menarche for girls and semenarche for boys) and secondary sex characteristics (e.g., enlargement of breasts for girls and deepening of the voice for boys) (Ponton & Judice, Reference Ponton and Judice2004). Simultaneously, adolescents’ cognitive abilities improve, which, in turn, stimulates abstract thinking and self-reflection (Christie & Viner, Reference Christie and Viner2005). Such self-reflection skills are especially imperative regarding the exploration and construction of one’s sexuality (e.g., determining one’s sexual orientation) (Ponton & Judice, Reference Ponton and Judice2004).

Adolescents typically respond to these developmental changes by communicating about their emerging sexual feelings and engaging in (non)coital sexual behaviors (e.g., self-masturbation) (DeLamater & Friedrich, Reference DeLamater and Friedrich2002). Scholars emphasize the active role of peers in these processes as they function as sources of support and inform adolescents on sexual strategies (e.g., how boys and girls flirt) and behavior (e.g., when to “lose” your virginity) (van de Bongardt et al., Reference Van de Bongardt, Yu, Deković and Meeus2015). However, the sexual socialization that adolescents receive from peers can also reinforce prevailing sexual stereotypes (e.g., sexual passiveness for girls; sexual dominance for boys) and sometimes contains erroneous information about, for instance, sexual protection (Ponton & Judice, Reference Ponton and Judice2004). Furthermore, romantic and sexual relationships offer a primary venue in which emerging sexual feelings are explored, experimented with, and responded to (Diamond & Savin-Williams, Reference Diamond, Savin-Williams, Lerner and Steinberg2009). During this explorative period adolescents will also further discover their preference for heterosexual, homosexual, and bisexual relationships. This exploration is typically more challenging for non-heterosexual adolescents (Saewyc, Reference Saewyc2011).

Although sexuality development is equally significant and profound among adolescent girls and boys, their experiences and perceived consequences do differ (Petersen & Hyde, Reference Petersen and Hyde2010). Such differences are often argued to be the product of biological and social factors. Biologically, differential hormonal influences bring along different developmental needs and body growth among boys and girls (Perry & Pauletti, Reference Perry and Pauletti2011). As for social factors, societal pressure typically leads individuals to conform to traditional gender roles (Ponton & Judice, Reference Ponton and Judice2004). These traditional gender roles coincide with the idea of a sexual double standard in which girls and women are expected to be sexually attractive and pleasing while ignoring their own sexual needs or even denying and shaming their sexual agency (Hamilton & Armstrong, Reference Hamilton and Armstrong2009). At the same time, sexual desire and agency is assumed to be inherent in male sexuality (Murray, Reference Murray2018).

Digital Media and Adolescents’ Sexuality

Over the past two decades, scholars have pointed to the increasing presence of digital media in the everyday lives of adolescents (Guse et al., Reference Guse, Levine and Martins2012). Owing to the rapid adoption of mobile devices (Ling & Bertel, Reference Ling, Bertel and Lemish2013), most adolescents have the possibility to be constantly online. Within this online environment, sexually oriented digital media activities take place in accordance to adolescents’ sexual development and needs. Such activities can be divided into two underlying themes: sexual health education and entertainment. Sexual health–related digital media activities include the use of websites and other online tools (e.g., social media banners) that cover sexual health information (e.g., contraceptive use, STDs, or menstrual cycles). Existing studies indicate that adolescents often turn to digital media in order to seek sexual information (e.g., Nikkelen et al., Reference Nikkelen, van Oosten and van den Borne2020).

Entertainment-related sexually oriented digital media encompasses adolescents’ uses of social media, sexting (via instant messaging tools of social media or mobile phone messages), and online pornography. Adolescents frequently use social media on a daily basis. When describing social media uses, the literature distinguishes between the private and public sphere in which interactions take place. Within the public sphere of social media, existing research mainly focuses on the posting of sexy selfies (e.g., van Oosten et al., Reference Van Oosten, de Vries and Peter2018) that encompasses 51.7% of adolescents’ self-presentations on social media (Kapidzic & Herring, Reference Kapidzic and Herring2015). Further, the public display of romantic affection and conflicts has also been the focus of existing studies (e.g., Rueda et al., Reference Rueda, Lindsay and Williams2015). Private social media use encompasses adolescents’ engagement in romantic relational communication (e.g., Young et al., Reference Young, Len-Ríos and Young2017) and even sexting via instant messaging tools (e.g., Van Ouytsel et al., Reference Van Ouytsel, Walrave and Ponnet2019).

The current literature defines sexting as “the sending of self-made sexually explicit messages, pictures or videos through the computer or mobile phone” (Van Ouytsel et al., Reference Van Ouytsel, Walrave and Ponnet2019, p. 216). This particular behavior takes place through instant messaging tools of social media and other digital applications, such as Snapchat, a tool that allows adolescents to send temporary available sexting messages to one (or multiple) person(s). A recent meta-analysis points to the relative commonness of sexting among adolescents, as one in ten adolescents has already engaged in this online behavior (Madigan et al., Reference Madigan, Ly, Rash, Van Ouytsel and Temple2018). This number is higher among girls and older adolescents (K. Cooper et al., Reference Cooper, Quayle, Jonsson and Svedin2016).

Apart from producing their own sexual material, adolescents, and especially boys, consume sexually explicit internet material (i.e., online pornography) that can also be described as an intimate sexually oriented digital media activity (Peter & Valkenburg, Reference Peter and Valkenburg2016). Exposure rates differ substantially depending on the examined countries in the literature. For instance, in the USA, 77% of adolescent boys and 33% of adolescent girls indicated that they had watched pornography in the past year (Hardy et al., Reference Hardy, Hurst, Price and Denton2019), while in Croatia, pornography use rates were higher among both adolescent boys (90%) and girls (43%) (Milas et al., Reference Milas, Klarić, Malnar, Šupe‐Domić and Slavich2019).

The Affordances of Sexually Oriented Digital Media

Sexually oriented digital media use is especially imperative in adolescents’ sexuality as their unique affordances (i.e., characteristics of digital media that provide the potential for a particular action) support the exploration of one’s sexuality. Specifically, Cooper and colleagues (Reference Cooper, Scherer, Boies and Gordon1999) identified three disinhibiting characteristics of online environments: (1) accessibility, (2) anonymity, and (3) asynchronous communication.

First, digital media are characterized by their accessibility to (the creation of) various forms of content related to intimacy, sexuality, and gender identity (e.g., Beals, Reference Beals2010). Given that some adolescents may lack sexual experiences or may feel too embarrassed to discuss intimate topics with others (in person) (e.g., how to wear a condom), the accessibility to a rich variety of sexual information through the online environment can be particularly helpful (Simon & Daneback, Reference Simon and Daneback2013). For example, adolescents can turn to sexual health websites to receive reliable information on intimate topics (e.g., Park & Kwon, Reference Park and Kwon2018). Also, via online pornography, adolescents have access to explicit information about sexual behaviors, attitudes, and gender roles (e.g., Grubbs et al., Reference Grubbs, Wright, Braden, Wilt and Kraus2019).

Second, digital media’s unique affordance to maintain one’s anonymity is useful for adolescents who are still discovering their sexuality. Peter and Valkenburg (Reference Peter and Valkenburg2011) point to two forms of anonymity: source anonymity and audiovisual anonymity. With source anonymity, adolescents have the ability to view or even distribute content of a sexual or romantic nature without the possibility to link this type of content to a particular individual or source. This extreme form of anonymity is especially appealing to adolescents when they are searching for sexually explicit content online (i.e., pornography). Specifically, adolescents can explore emerging sexual feelings without the risk of being discovered and, consequently, feeling ashamed afterwards (Shek & Ma, Reference Shek and Ma2016).

With audiovisual anonymity, the lack or the reduction of nonverbal cues (i.e., visual or auditory) in online communication is captured (Peter & Valkenburg, Reference Peter and Valkenburg2011). When adolescents engage in sexting or communicate through instant messaging tools, they can choose to only communicate through linguistic/textual/verbal content or to use visual and/or audio cues. Adolescents typically have high levels of self-awareness and are, as a result, often more shy in traditional face-to-face interactions (e.g., Weil et al., Reference Weil, Fleming and Dumontheil2013). Within digital contexts, audiovisual anonymity can facilitate discussions about intimate topics or themes, while such discussions may be more likely perceived as awkward in offline environments (Van Ouytsel et al., Reference Van Ouytsel, Van Gool, Walrave, Ponnet and Peeters2016b).

Third, the ability to communicate asynchronously is another relevant affordance to understand the role of digital media in adolescents’ sexuality. Through instant messaging tools, adolescents have the opportunity to (privately) communicate with others about sexual or romantic topics while having a heightened sense of control over their conversations (Le et al., Reference Le, Temple, Peskin, Markham, Tortolero, Weins and Hiestand2014). In contrast to face-to-face communication, adolescents are able to edit and think about how they communicate about their emerging sexual or romantic feelings and, thus, learn at their own pace how to have a proper and respectful conversation about intimate topics (Van Ouytsel et al., Reference Van Ouytsel, Van Gool, Walrave, Ponnet and Peeters2016b).

The affordances of accessibility, anonymity, and asynchronous communication are shared over differential sexually oriented digital media. Other affordances are more platform specific. For example, when adolescents send photos via Snapchat to another person, the visual content is only accessible to the receiver for a maximum of 30 seconds. On Facebook, on the other hand, pictures sent through Messenger are permanently accessible until the receiver deletes them. The temporary accessibility of posts is described in the literature as ephemeral content (Chen & Cheung, Reference Chen and Cheung2019). This and other platform-specific affordances are assumed to further play a key role in adolescents’ choices to use a certain type of digital media platform as a response to specific sexual or romantic relational needs within a particular context.

Motivations for Adolescents’ Digital Media Uses within the Context of Sexuality

Uses and gratifications theory denotes that users’ media interactions are driven by differential motivations (Katz et al., Reference Katz, Blumler and Gurevitch1973). Such motivations are entwined with digital media affordances as some affordances create new motivations when new media are introduced (Sundar & Limperos, Reference Sundar and Limperos2013). Within the context of sexually oriented digital media and adolescents’ sexuality, motivations differ from each other in terms of the motivational source (i.e., internal vs. external). As for internal motivations, research points toward sexual exploration, and relationship initiation and maintenance. As for external (or other-imposed) motivations, pressure and coercion have been identified as motivators. The section below discusses these motivations in terms of their meaning and how digital media use driven by a particular motivation affects adolescents’ sexuality.

Sexual Exploration

One of the most commonly reported motivations for adolescents’ sexual engagement with digital media is the need to explore one’s sexuality and emerging sexual feelings (Cooper et al., Reference Cooper, Quayle, Jonsson and Svedin2016). Particularly, heightened levels of arousal and sexual curiosity characterize adolescence and are the predominant reasons for using online pornography and engaging in sexting. Gender differences are relevant in this context as boys are more often driven by arousal, pleasure, and sexual curiosity than girls (Cooper et al., Reference Cooper, Quayle, Jonsson and Svedin2016; Grubbs et al., Reference Grubbs, Wright, Braden, Wilt and Kraus2019).

Apart from arousal and curiosity, adolescents share a desire to learn about sexual practices as many of them are still inexperienced. Adolescents frequently turn to sexually oriented digital media stimulated by the need for information about sexual activities (e.g., how to initiate intercourse) (Pascoe, Reference Pascoe2011). This information-seeking need is especially relevant in terms of adolescents’ online pornography use as this type of digital content explicitly shows how to engage in different types of sexual activities. Moreover, pornographic content can even be used as a source of inspiration for novel sexual behaviors (e.g., learning about different sexual positions) (e.g., Grubbs et al., Reference Grubbs, Wright, Braden, Wilt and Kraus2019).

The need to construct one’s sexual and gender identity is further considered to be a key motivator of adolescents’ uses of sexually oriented digital media. Specifically within the online environment, adolescents feel more secure and less prejudiced when exploring and, even, expressing their own sexuality and gender identity (e.g., Pascoe, Reference Pascoe2011). On social media, adolescents are exposed to varying types of sexual content (e.g., sexy selfies) shared by peers and other significant actors (e.g., influencers). This content offers insights on how adolescent girls and boys behave and present themselves sexually (e.g., Kapidzic & Herring, Reference Kapidzic and Herring2015; Shafer et al., Reference Shafer, Bobkowski, Brown and Dill2013). These self-presentations stimulate adolescents to explore their own sexuality (van Oosten et al., Reference Van Oosten, Peter and Boot2015). Online sexual self-presentations express different sexual beliefs, preferences, and behaviors whilst simultaneously negotiating peer approval and acceptance. For heterosexual boys and girls, these self-presentations often reflect traditional gender stereotypes. Girls are more invested in portraying themselves as sexually attractive and seductive, whereas boys’ self-presentations are more varied (e.g., pictures of oneself practicing hobbies) (Kapidzic & Herring, Reference Kapidzic and Herring2015). Indeed, the longitudinal study of van Oosten et al. (Reference Van Oosten, Vandenbosch and Peter2017b) shows that adolescents who hold more gender stereotypical beliefs present themselves online more in a sexy way and, at the same time, are also more exposed to sexy self-presentations.

Further, scholars point to the experimental nature of consensual sexting by which adolescents can establish their sexuality. Adolescents are motivated to experiment with different sexual experiences whilst expressing their own sexual preferences (e.g., Dir et al., Reference Dir, Coskunpinar, Steiner and Cyders2013). Also, through the uses of online pornography, adolescents are exposed to different types of sexual activities that allows them to explore their sexual preferences freely. This exploration of one’s sexual preferences facilitates the acceptance and establishment of, for example, one’s sexual orientation (Grubb et al., Reference Grubbs, Wright, Braden, Wilt and Kraus2019).

Relationship Initiation and Maintenance

One frequently reported motivator of digital media uses is the ability to initiate a romantic relationship and even maintain this relationship. Adolescents use social media and sexting in order to flirt with someone, ask someone out for a date, or even ask someone to be their boyfriend/girlfriend (Young et al., Reference Young, Len-Ríos and Young2017). For sexual minority groups, relationship initiation through digital media is particularly convenient. These groups often experience difficulties forming romantic relationships offline as they have fewer potential romantic partners and experience stigmatizations or even physical harm (Williams et al., Reference Diamond, Savin-Williams, Lerner and Steinberg2009). Within the online environment, sexual minority groups can experience less harassment and feel more secure when establishing a romantic relationship (Korchmaros et al., Reference Korchmaros, Ybarra and Mitchell2015).

Further, in order to maintain one’s romantic relationship, adolescents also turn to digital media. Particularly, when a relationship is established, adolescents can advertise the relationship status on Facebook (i.e., “in a relationship” or “engaged”), which can be seen as an important step in their romantic relationship (Van Ouytsel et al., Reference Van Ouytsel, Van Gool, Walrave, Ponnet and Peeters2016b). Also, instant messaging tools on social media allow romantic partners to stay in contact while being physically distant (Utz & Beukeboom, Reference Utz and Beukeboom2011). Scholars even suggest that digital communication is now an integral part of adolescent couple functioning (Blumer & Hertlein, Reference Blumer, Hertlein and Breuss2015). In order to maintain a more intimate bond with one’s romantic partner, adolescents often use sexting (Cooper et al., Reference Cooper, Quayle, Jonsson and Svedin2016). Consensual sexting is considered a normal and contemporary form of sexual expression and intimate communication within relationships (Burkett, Reference Burkett2015; Parker et al., Reference Parker, Blackburn, Perry and Hawks2013). Further, sexting can initiate offline sexual behaviors with romantic partners. For adolescents who are physically separated or cannot engage in sexual activities with their romantic partners (e.g., because this is forbidden by their religion), sexting can take place as a means of sustaining a level of intimacy (Cooper et al., Reference Cooper, Quayle, Jonsson and Svedin2016).

Pressure

Similar to offline sexual behavior, online sexual behavior can also be motivated by external factors such as peer and partner pressure. Peers become increasingly important in the lives of adolescents as they are experiencing elevated need for autonomy from one’s parents and, at the same time, seek out approval from their peers (Lerner et al., Reference Lerner, Boyd, Du, Weiner and Craighead2010). Such dynamics are also relevant when considering sexually oriented digital media uses. Particularly, studies consistently demonstrate that the need to conform to peer norms and even the experience of peer pressure are significant motivators for adolescents’ posting of sexy selfies on social media (i.e., mostly among girls) (de Vaate et al., Reference de Vaate, Veldhuis, Alleva, Konijn and van Hugten2018; Mascheroni et al., Reference Mascheroni, Vincent and Jimenez2015), online pornography use (i.e., mostly among boys) (Chen et al., Reference Chen, Leung, Chen and Yang2013; Vanden Abeele et al., Reference Le, Temple, Peskin, Markham, Tortolero, Weins and Hiestand2014), and sexting (Dake et al., Reference Dake, Price, Maziarz and Ward2012; Maheux et al., Reference Maheux, Evans, Widman, Nesi, Prinstein and Choukas-Bradley2020).

Scholars emphasize that sexting can also be initiated after experiencing pressure from a partner. Especially girls experience such (implicit) pressure from partners (Walrave et al., Reference Walrave, Heirman and Hallam2014). Girls often believe that they need to send self-produced sexual images to their partners in order to maintain a good relationship. Boys, on the other hand, experience more pressure from other peers as the ability to chat to girls and negotiate access to seeing their bodies proves their dominant sexual status (Crofts et al., Reference Crofts, Lee, McGovern, Milivojevic, Grealy, Driscoll and Hickey-Moody2018). Sexual activities with girls (e.g., receiving girls’ sexual pictures and forwarding these pictures without their consent) can thus help boys to gain peer status and popularity (Burén & Lunde, Reference Burén and Lunde2018; Ringrose et al., Reference Ringrose, Harvey, Gill and Livingstone2013). At the same time, more moral responsibility is attributed to girls for sending a sexting picture than for boys forwarding such pictures without consent. As such, regardless of whether they engage in sexting or not, girls’ behavior seems to be consistently evaluated in terms of sexist norms (Lippman & Campbell, Reference Lippman and Campbell2014; Ringrose et al., Reference Ringrose, Harvey, Gill and Livingstone2013).

Sexual Coercion

A growing body of literature indicates that the uses of sexually oriented digital media can also take place in a context of abusive dating behaviors (Van Ouytsel et al., Reference Van Ouytsel, Ponnet, Walrave and Temple2016a). Reed and colleagues (Reference Reed, Tolman and Ward2017) distinguished three different types of digital dating abuse among adolescents: digital monitoring and controlling, direct aggression, and sexual coercion. Digital monitoring/controlling is the most frequently reported digital abusive behavior. This particular type of abusive behavior entails the intrusion of a partner’s privacy via controlling their online activities and relationships (Dracker & Martsolf, Reference Draucker and Martsolf2010). Girls have reported a higher frequency of digital monitoring than boys (Reed et al., Reference Reed, Tolman and Ward2017).

Further, digital direct aggression toward one’s romantic partner or dating interest can also be a motivation for adolescents’ engagement with digital media. Such direct aggression can, for example, be expressed by posting a hurtful public/private message or the threat to physically harm one’s partner (Borrajo et al., Reference Borrajo, Gámez-Guadix and Calvete2015).

Lastly, scholars stress the occurrence of digital sexual coercion among adolescents. This behavior encompasses the use of sexually oriented digital media to pressure someone to send intimate pictures, redistributing intimate pictures without consent, and even threatening with sexual harm (Hellevik, Reference Hellevik2019). Boys engage more regularly in digital sexual aggression and coercion (Reed et al., Reference Reed, Tolman and Ward2017, Reference Reed, Ward, Tolman, Lippman and Seabrook2018). In terms of digital sexual coercion, the previous section has already addressed partner pressure being a detrimental motivator for, mostly girls’, sexting behaviors. When addressing sexting behavior in adolescents and its problematic motivators, it is especially crucial to emphasize the occurrence of grooming. This online behavior is often considered a criminal offence and entails a process in which an adult manipulates a minor via digital media in order to obtain sexual materials from them or to sexually abuse them (Machimbarrena et al., Reference Machimbarrena, Calvete, Fernández-González, Álvarez-Bardón, Álvarez-Fernández and González-Cabrera2018). For instance, 16.6% of adolescents indicated that they had experienced grooming online (Machimbarrena et al., Reference Machimbarrena, Calvete, Fernández-González, Álvarez-Bardón, Álvarez-Fernández and González-Cabrera2018).

Theoretical Frameworks for Effects of Sexually Oriented Digital Media Use

Several theoretical frameworks can be used to clarify how the effects of sexually oriented digital media take place. Within the literature, social cognitive theory, sexual script theory, and self-effects literature are typically proposed to explain the effects of the uses of these media (i.e., social media, sexting, and online pornography).

Social cognitive theory (Bandura, Reference Bandura2001) is frequently referred to as a traditional theoretical model that is consistently adopted by scholars examining the sexual effects of traditional media (e.g., television). Over the past two decades, this theoretical framework has also proven to be useful to explain digital media effects as the tenets of this theory are transferable to an online environment. Social cognitive theory argues that behavioral and attitudinal effects are contingent on expectancies of such behaviors and attitudes. Within the context of digital media, expectancies are shaped by the observation of attractive models being rewarded for the engagement in or sharing of certain sexual behaviors online or the expression of particular sexual beliefs. For example, digital media users can observe peers on social media or actors of pornographic videos which operate as “attractive models.” These models are rewarded, for example through likes (for peers on social media) or sexual satisfaction (for actors in pornographic videos) for the engagement in or sharing of certain sexual behaviors or beliefs. Peers can, for example, share a status update that implies that they had casual sex or post an article about gender equality, while actors in pornographic movies more explicitly engage in casual sex. By observing these rewarded sexual behaviors as well as beliefs promoted by attractive models, digital media users learn which behaviors and beliefs are socially acceptable and positively reinforced. As such, these behaviors and beliefs have a higher chance of being adopted by digital media users. Social cognitive theory further points to the mechanisms explaining the adoption of certain sexual behaviors. In this context, sexual media effects are not produced immediately but operate via underlying processes. Sexual cognitions, such as sexual self-efficacy (i.e., one’s beliefs about one’s ability to control a sexual behavior or situation), often operate as factors explaining the link between media use and behavioral outcomes.

Building on the principles of social cognitive theory, sexual script theory (Gagnon & Simon, Reference Gagnon and Simon1973) offers an additional theoretical framework conceptualizing how sexual media messages shape users’ sexual behaviors. Although this theory was initially created to explain the impact of sexual content in traditional media, its tenets can also be adopted to explore the implications of sexual messages in the digital environment. Within the context of digital media uses, sexual script theory argues that online sexual content is stored in users’ memories and operates as a “script” to guide their future sexual behavior. For example, when digital media users observe how other couples behave on social media (e.g., expressing their love for each other), they can “store” this information and use it to guide their (online) behaviors within a romantic relationship. In pornographic content, these sexual scripts are shown more explicitly, offering digital media users more practical guidelines on how to engage in sexual activities. The retrieval of these sexual scripts from one’s memory is facilitated through activation and recency processes. In particular, the more often and/or the more recently sexual scripts are observed, the more likely users are to engage in such behaviors endorsed by the scripts.

Although these two traditional theoretical frameworks can explain exposure effects of digital media, they cannot clarify all effects and processes within the online environment. Social media and sexting allow users to create and distribute content themselves. This ability to create and distribute online sexual content can also have substantial implications for the media users themselves. In recent years, scholars have recognized such effects and described them as self-effects, which generally constitutes “the effects of messages on the cognitions, emotions, attitudes and behavior of the message creators/senders themselves” (Valkenburg, Reference Valkenburg2017, p. 478).

Two mechanisms, namely self-perception and self-presentation processes, are especially relevant when clarifying these sexual self-effects. In terms of self-perception processes, Bem (Reference Bem and Berkowitz1972) argues that individuals ascertain their self-concepts by retrospectively observing their own behaviors. Within the context of digital media, self-perception processes are triggered by the observations of the content media users share or the behavior they depict online. For example, through the sharing of sexy selfies online or the description of certain adventurous sexual behaviors via sexting, digital media users can verify that they are respectively sexy or sexually adventurous (e.g., van Oosten et al., Reference Van Oosten, de Vries and Peter2018).

Another key mechanism of self-effects of the online environment, namely the occurrence of self-presentation processes, is especially relevant within the context of social media. In particular, digital media users have the ability to carefully select which information to share on social media platforms regarding their sexual beliefs and/or behaviors. Therefore, media users will first reflect elaborately on how to present themselves online by engaging in a process called biased scanning. Particularly, by envisioning their desired and ideal online sexual selves, media users will search for information about certain sexual characteristics in their memory that can help to create such desired self-presentations online (Valkenburg, Reference Valkenburg2017). For example, when adolescents focus on certain physical attributes when sharing sexy selfies (e.g., for girls their cleavage, for boys their muscles) the evaluation of these characteristics makes them more accessible in media users’ memories that can, in turn, affect self-evaluations (Schlenker et al., Reference Schlenker, Dlugolecki and Doherty1994). Moreover, individuals tend to strive for consistency in terms of the way they present themselves to others. This need for consistency can increase the likelihood that online self-presenters will continue to express the same sexual beliefs and/or engage in the same sexual behaviors as they do online (i.e., public commitment; Kelly & Rodriguez, Reference Kelly and Rodriguez2006).

Digital Media and Its Implications for Adolescents’ Sexuality

A vast and still growing body of literature has examined adolescents’ sexually oriented digital media uses and their effects on adolescents’ sexuality. Below, the conclusions of this body of work are summarized regarding four types of sexual self-development outcomes (i.e., sexual self-concept, sexual agency, sexual certainty, and sexual satisfaction), three types of attitudinal outcomes (i.e., sexually permissive attitudes, gender stereotypical sexual beliefs, and sexual objectification), two relationship quality indicators (i.e., commitment and sexual attraction), and three types of behavioral outcomes (i.e., sexual activities, risky sexual behavior, and sexual aggression). These outcomes are all significant aspects in the context of adolescents’ sexuality development. We also discuss existing literature on underlying processes (e.g., sexual arousal) that can explain the relationship between sexually oriented digital media use and sexual outcomes. Further, if relationships were conditional (e.g., a stronger effect based on adolescents’ gender) this will also be addressed.

Sexual Self-Development Outcomes
Sexual Self-Concept

The construction of the sexual self-concept can be described as adolescents’ understanding of their sexual selves and attributes that define them as a sexual person. The literature shows that social media and sexting play an important role in the construction of this self-concept and, therefore, guide adolescents in their understanding of their sexual selves. Particularly, with regards to social media, sexy online self-presentations appear to be especially relevant. The study of van Oosten et al. (Reference Van Oosten, de Vries and Peter2018) demonstrated that such self-presentations can define adolescents’ sexual self-concept over the course of six months, and are also driven by one’s sexual self-concept. This means that not only are sexy self-presentations on social media used as guidance for adolescents to understand their own sexual selves, but the way adolescents view themselves sexually also guides the way they present themselves online (Bobkowski et al., Reference Bobkowski, Shafer and Ortiz2016). Relatedly, when it comes to sexting, the literature has demonstrated that when adolescents sext, they have a more developed sexual self-concept in comparison to adolescents who do not sext (Marengo et al., Reference Marengo, Settanni and Longobardi2019). As such, this implies that sexting may help adolescents in their understanding and exploration of their own sexual selves, such as discovering to whom they are attracted to.

Sexual Agency

As for sexual agency, which entails the ability to communicate and negotiate about one’s sexuality, the literature seems to be relatively scarce when it comes to adolescents’ sexually orientated digital media uses. Only the study of Klein et al. (Reference Klein, Šević, Kohut and Štulhofer2020) has explored this sexual outcome in relation to adolescents’ pornography uses. They demonstrated that the more girls view pornography online, the more sexually agentic they feel over time. This outcome is especially relevant for girls, as scholars have previously highlighted girls’ lack of attention for their own sexual desires (Cheng et al., Reference Cheng, Hamilton, Missari and Ma2014). Therefore, pornography may offer a useful tool for girls to take ownership of their own sexual desires and express what they want sexually. In contrast, social media may be detrimental for adolescents’ sexual agency. Among young adults, Facebook involvement appears to predict a decreased sexual assertiveness through mechanisms of objectified body consciousness (Manago et al., Reference Manago, Ward, Lemm, Reed and Seabrook2015). These findings suggest that similar processes can occur among adolescents. However, this assumption has not been tested yet among adolescents, nor can conclusions be made about the directionality of this relationship given that the research has primarily been correlational at just one time point.

Sexual Certainty

A large body of online pornography studies has explored how this online sexual media use can affect other important factors of adolescents’ sexuality, such as their sexual certainty. Studies show that the more adolescents watch pornography online, the more they feel uncertain about their sexual beliefs and values (e.g., Peter & Valkenburg, Reference Peter and Valkenburg2008, Reference Peter and Valkenburg2010; van Oosten et al., Reference Van Ouytsel, Ponnet, Walrave and Temple2016a). This relationship has been demonstrated to occur via adolescents’ involvement with pornographic content (Peter & Valkenburg, Reference Peter and Valkenburg2010), and girls appear to be more affected than boys (Peter & Valkenburg, Reference Peter and Valkenburg2010; van Oosten et al., Reference Van Ouytsel, Ponnet, Walrave and Temple2016a).

Sexual Satisfaction

In terms of sexual satisfaction (i.e., the degree to which one is satisfied with one’s sexual life), scholars point to the likelihood that adolescents’ sexting behavior can promote a greater sexual satisfaction in adolescents (Van Ouytsel et al., Reference Van Ouytsel, Walrave and Ponnet2019) as such relations have been found among adults (Galovan et al., Reference Galovan, Drouin and McDaniel2018). However, as of yet, no research has explored this particular question in youth. In terms of pornography, on the other hand, it appears that adolescents’ uses of this online sexual media negatively affects their sexual satisfaction in the long term (Doornwaard et al., Reference Doornwaard, Bickham, Rich, Vanwesenbeeck, van den Eijnden and ter Bogt2014; Peter & Valkenburg, Reference Peter and Valkenburg2006). This means that the more adolescents view online pornography, the less satisfied they are with their own sexual lives. This link is stronger for adolescents who have little to no sexual experience and adolescents who perceive that the majority of their peers are sexually inexperienced.

Attitudinal Outcomes
Sexually Permissive Attitudes

In different types of online sexual content (e.g., sexual self-presentations or pornographic content), sexual activities are predominantly portrayed or referred to as casual and risk-free, without paying attention to the emotional (e.g., fear of being rejected) and physical complexities (e.g., properly using a condom) of these activities (e.g., Carrotte et al., Reference Carrotte, Davis and Lim2020). Such content has been demonstrated to have a significant impact on the development of sexually permissive attitudes among adolescents. Sexually permissive attitudes can be conceptualized as an inclusive category, generally constituting positive attitudes toward sex with casual partners.

The literature indicates that social media and pornography use contribute to the development of such permissive attitudes. Longitudinal research shows a long-term link over the course of one year between adolescents’ time looking at sexual online self-presentations of others and increased willingness to engage in casual sex (van Oosten et al., Reference Van Oosten, Peter and Vandenbosch2017a). Thus, the more adolescents are exposed to sexy online presentations of others, the more they hold positive attitudes toward sex with casual partners. Moreover, looking at others’ self-presentation on social media predicts an increase in adolescents’ perception of the amount of same-aged friends engaging in casual sex, which in turn predicts an increase in their own willingness to engage in casual sex themselves. Finally, the more adolescents watch pornographic content online, the more they hold positive attitudes toward casual sex (e.g., Baams et al., Reference Baams, Overbeek, Dubas, Doornwaard, Rommes and Van Aken2015; Brown & L’Engle, Reference Brown and L’Engle2009; Doornwaard et al., Reference Doornwaard, Bickham, Rich, ter Bogt and van den Eijnden2015) especially among adolescents who perceive pornography as realistic (Baams et al., Reference Baams, Overbeek, Dubas, Doornwaard, Rommes and Van Aken2015) and among boys (Doornwaard et al., Reference Doornwaard, Bickham, Rich, ter Bogt and van den Eijnden2015; Brown & L’Engle, Reference Brown and L’Engle2009).

Gender Stereotypical Beliefs

Digital media also reinforces traditional sexual gender stereotypes. These gender stereotypical beliefs include the assumption that men are more sexually assertive and dominant, and that women lack sexual agency and are more passive. Double standards are commonly embedded within these gender stereotypes as, for example, women are expected to be sexually reluctant while, simultaneously, they are also highly sexualized and valued based on their sexual attractiveness (Popa & Gavriliu, Reference Popa and Gavriliu2015).

Pornography use contributes to the development of gender stereotypes as these beliefs are reflected in the content and uses of online pornography. Not only do studies point to online pornography depicting men and women in a gender stereotypical manner (e.g., Klaassen & Peter, Reference Klaassen and Peter2015), but its uses are also highly gendered as online pornography is typically targeted at men and perceived by both boys and girls as “manly” behaviors (e.g., Scarcelli, Reference Scarcelli2015). Cross-sectional (To et al., Reference To, Ngai and Iu Kan2012) and longitudinal studies (Brown & L’Engle, Reference Brown and L’Engle2009) consistently find that the more adolescents are exposed to online pornography, the more they hold gender-stereotypical and, even, sexist beliefs.

Sexual Objectification

Although a growing body of literature points to the occurrence of sexualizing practices in digital media, especially toward women (Ringrose, Reference Ringrose, Gill and Scharf2011), few studies have addressed how digital media can contribute to the development of adolescents’ sexually objectifying beliefs. These beliefs generally constitute the evaluation of an individual based on their sexual attractiveness and sexually instrumental value (Fredrickson & Roberts, Reference Fredrickson and Roberts1997).

In terms of social media use, while holding more sexually objectifying beliefs increased exposure to sexy self-presentations of others for young adolescents in one study, this exposure did not further increase such beliefs (van Oosten et al., Reference Van Oosten, Peter and Boot2015). Further, in terms of adolescents’ engagement in sexting behavior, scholars have expressed concerns regarding the possible sexually objectifying practices that may occur when one sends or receives sexually explicit pictures (Ringrose & Harvey, Reference Ringrose and Harvey2015). Nevertheless, no research has yet explored such possible mechanisms (K. Cooper et al., Reference Cooper, Quayle, Jonsson and Svedin2016).

As for online pornography, existing content analytical research stresses that pornographic content is saturated with sexually objectifying practices (especially toward women) (Carrotte et al., Reference Carrotte, Davis and Lim2020; Klaassen & Peter, Reference Klaassen and Peter2015). Both cross-sectional (e.g., Maes et al., Reference Maes, Schreurs, van Oosten and Vandenbosch2019) and longitudinal studies (e.g., Peter & Valkenburg, Reference Peter and Valkenburg2009, Reference Peter and Valkenburg2011) have documented that the more adolescents watch online pornography, the more they sexually objectify women. Such beliefs even explain the relationship between exposure to online pornography and acceptance of rape myths (Burt, Reference Burt1980; Maes et al., Reference Maes, Schreurs, van Oosten and Vandenbosch2019). The acceptance of rape myths can have negative implications for adolescents’ future sexuality since it can be related to sexual coercion perpetration (Trottier et al., Reference Trottier, Benbouriche and Bonneville2021).

Relationship Quality Indicators
Commitment

To understand romantic relational outcomes of adolescents’ digital media uses, digital media applications can be distinguished by users’ abilities to communicate, create content, or be exposed to content within a public sphere (e.g., Facebook wall or online pornography) versus a private sphere (e.g., instant messaging tools of social media or sexting behavior). These different contexts shape the occurrence of different romantic relational outcomes in adolescents. With regards to the private sphere of instant messaging tools or sexting behavior, scholars point to its beneficial implications for adolescents’ perceived romantic relationship quality. Specifically, a growing body of studies has emphasized that adolescents’ online communication with romantic partners improves levels of trust, commitment, communication, and security (e.g., Blais et al., Reference Blais, Craig, Pepler and Connolly2008; Morey et al., Reference Morey, Gentzler, Creasy, Oberhauser and Westerman2013). Moreover, as previously mentioned, the ability to post about one’s relationship in the public online sphere allows adolescents to express their love for their partners (Utz & Beukeboom, Reference Utz and Beukeboom2011). However, research is lacking regarding the possible negative or positive implications of such online behavior among adolescents.

Existing research does emphasize that when adolescents are active in the online public sphere, they can also be confronted with other profiles that can be perceived as “romantic competition.” Both qualitative and quantitative research has demonstrated that such online experiences evoke feelings of jealousy and distrust among adolescents (e.g., Rueda et al., Reference Rueda, Lindsay and Williams2015). Moreover, the literature also points to the possibility that the exposure to alternative partners on social media may have negative implications for adolescents’ relationship commitment (de Lenne et al., Reference de Lenne, Vandenbosch, Eggermont, Karsay and Trekels2018).

Sexual Attraction

Another indicator of relationship quality is one’s sexual attraction to one’s partner. In this view, sexting may be especially relevant for adolescents’ sexual attraction for their partner. For instance, the more adolescents engage in this online sexual behavior, the higher their feelings of sexual attraction, passion, and sexual arousal toward their partner (van Ouytsel et al., Reference Van Ouytsel, Walrave and Ponnet2019).

Behavioral Outcomes
Sexual Behavior

Sexually oriented digital media can play an important role in adolescents’ engagement in sexual activities. Longitudinal studies have concluded that the more adolescents use social media, the more sexually experienced they are (Reitz et al., Reference Reitz, van de Bongardt and Baams2015; van Oosten et al., Reference Van Oosten, Peter and Boot2015). Sexting seems to promote sexual behavior in adolescents including higher sexual activity (e.g., MacDonald et al., Reference MacDonald, Imburgia, Auerswald and Ott2018) and having multiple sexual partners (e.g., Romo et al., Reference Romo, Garnett and Younger2017). In terms of having multiple sexual partners, the literature indicates that this link is stronger among boys than girls (Mori et al., Reference Mori, Temple, Browne and Madigan2019).

As for adolescents’ uses of online pornography, both cross-sectional (e.g., Donevan & Mattebo, Reference Donevan and Mattebo2017) and longitudinal studies (Brown & L’Engle, Reference Brown and L’Engle2009) have demonstrated that the more adolescents watch such sexual content online, the higher their likelihood of having (casual) sexual intercourse with multiple sexual partners.

Risky Sexual Behavior

In regard to risky sexual behaviors, the current chapter refers to sexual behaviors (under the influence of drugs) that contribute to unintended pregnancy and the transmission of STIs. A recent meta-analysis points to the role of adolescents’ social media use in the engagement in risky sexual behaviors (Vannucci et al., Reference Vannucci, Simpson, Gagnon and Ohannessian2020). Furthermore, consistent correlational evidence has emerged that the more adolescents sext, the less they use contraception during sexual interactions (e.g., Rice et al., Reference Rice, Craddock and Hemler2018). When it comes to adolescents’ online pornography use, results regarding risky sexual behavioral outcomes are inconsistent. Specifically, some studies find that the more adolescents view pornography, the riskier their sexual behaviors (e.g., Luder et al., Reference Luder, Pittet, Berchtold, Akré, Michaud and Surís2011), while others indicated that there is no such link (e.g., Peter & Valkenburg, Reference Peter and Valkenburg2011).

Sexually Aggressive Behavior

Within the field of sexting research, specific attention has been paid to the occurrence of this online behavior as a form of sexual coercion or harassment (K. Cooper et al., Reference Cooper, Quayle, Jonsson and Svedin2016). Specifically, sexting can entail forms of sexual aggression, sexual pressure, and harassment (e.g., through nonconsensual forwarding of sexually explicit pictures). However, limited knowledge exists regarding the offline consequences of negative forms of sexting behavior. In one study, Choi and colleagues (Reference Choi, Van Ouytsel and Temple2016) highlighted the association between offline sexual coercion (e.g., being pressured to engage in sexual activities) and sexting behavior among girls.

In terms of adolescents’ online pornography use, a link was found with sexual harassment perpetration among boys (Brown & L’Engle, Reference Brown and L’Engle2009). Further, the literature points to the necessity of addressing the type of online pornographic content in the context of sexual aggression research. For instance, only exposure to violent online pornography predicts higher sexual assault perpetration among adolescents (Ybarra et al., Reference Ybarra, Mitchell, Hamburger, Diener-West and Leaf2011).

Challenges and Future Directions

For the past two decades, growing attention has been given to adolescents’ sexually oriented digital media uses. Not only has the literature pointed to positive motivations of these online media applications (e.g., construction of sexuality), but also to harmful and negative reasons to use sexually oriented digital media (e.g., relationship monitoring). These uses have been demonstrated to shape different outcomes related to sexual self-development (e.g., sexual agency), sexual attitudes (e.g., gender stereotypical beliefs), relationship quality (e.g., commitment), and sexual behaviors (e.g., risky behaviors). By focusing on the unique developmental context of adolescents and, thus, stressing their receptiveness for sexual content, the majority of the studies have pointed to detrimental sexual outcomes of digital media uses. However, scholars have recently emphasized that the predominant attention to negative outcomes and, simultaneously, a systematic inattention to positive outcomes, cannot provide a comprehensive and nuanced understanding of digital media effects (de Leeuw & Buijzen, Reference de Leeuw and Buijzen2016). As such, it may be possible that positive digital media effects in the context of adolescents’ sexuality are undiscovered. This shortcoming introduces our first and most important suggestion for future research.

Specifically, we first encourage future research to explore beneficial implications of digital media uses for adolescents’ sexuality and future sexual identity and experiences. Future studies are recommended to adopt a positive psychology framework when exploring adolescents’ digital media uses. In this framework, positive and beneficial experiences, traits, and underlying mechanisms facilitating such experiences are explored (Seligman & Csikszentmihalyi, Reference Seligman, Csikszentmihalyi and Csikszentmihalyi2014). It is fundamental to note, however, that the adoption of this positive psychology paradigm should be considered as an addition to the current knowledge in order to present a balanced and more exhaustive understanding of adolescents’ digital media uses. With the occurrence of different social movements striving for, for example, LGBTQ+ rights (e.g., #pride), adolescents are exposed to online prosocial sexual content (e.g., messages that promote a positive sexuality). Such exposure may have a beneficial impact on adolescents’ understanding of others’ sexualities. Moreover, with the engagement in sexting or the uses of online pornography, adolescents may be more aware and accepting of their own physical sexual feelings. Other positive influences of sexual digital media on young users may include sexual empowerment, an increased sexual knowledge, or other outcomes related to the adolescent’s well-being.

As for gender stereotypical beliefs, social media, and in particular online videosharing sites (e.g., YouTube), despite still being restricted by standards of femininity or masculinity (Molyneaux et al., Reference Molyneaux, O’Donnell, Gibson and Singer2008; Wotanis & McMillan, Reference Wotanis and McMillan2014), have shown to be spaces that support a change in gendered ideology among youth (Morris & Anderson, Reference Morris and Anderson2015). For instance, male vloggers challenge masculine stereotypes by being emotionally open and embracing of their femininity, supporting gender equality and homosexual rights (Morris & Anderson, Reference Morris and Anderson2015), in particular by using satire and parody (Maloney et al., Reference Maloney, Roberts and Caruso2018; Wotanis & McMillan, Reference Wotanis and McMillan2014).

Second, the current chapter draws attention to the limited knowledge on underlying processes, such as peer norms or physical responses (e.g., arousal), which may explain the (possible) link between adolescents’ digital media uses and detrimental and beneficial sexuality outcomes. The lack of longitudinal and experimental research, which is needed to interpret complex response states elicited by digital media uses, may explain this gap in the literature. The exploration of underlying processes explaining sexual digital media effects is crucial though, as it can provide a more comprehensive understanding of key processes explaining why certain media effects occur. Thus, future research, more specifically longitudinal and experimental studies, is strongly recommended to further examine such indirect processes.

Lastly, studies have largely adopted cross-sectional designs, especially in terms of exploring adolescents’ sexting behavior and their effects. By following such designs, the literature has only examined the unidirectional nature of sexual digital media effects. More importantly, due to these designs, the direction of the relationships often cannot be established. Moreover, it is likely that the link between adolescents’ digital media uses and sexuality is reciprocal and bi-directional. Specifically, we point to adolescents’ agency to select digital media or create digital content shaped by their personal characteristics (e.g., pubertal status), sociocultural context (e.g., peers or Western culture), lived experiences, and expectancies of such media uses and content creation. Scholars postulate that adolescents’ selection of digital media and content creation and the outcomes of such media uses are two interacting processes. As such, it could be possible that adolescents’ existing sexual attitudes, experiences, or behaviors guide the selection of specific digital media applications (e.g., instant messaging tools) or the creation of sexual content (e.g., sexy self-presentations) that, in turn, can strengthen such sexual attitudes or behaviors or make them more susceptible to other attitudinal, experiential, and/or behavioral influences. As such, the third recommendation for future research is to examine the possible bidirectional nature of links between adolescents’ digital media uses and sexuality-related outcomes.

In sum, the literature shows that sexually oriented digital media use can play an important role in adolescents’ sexual socialization. Unique affordance of these media, such as its accessibility, invite to use sexually oriented digital media for the development of sexual selves, relationships, sexual attitudes, and behaviors. Several theories, such as social cognitive theory, can explain why adolescents use such media and how it may shape their sexuality. This chapter advises future research to explore, next to antisocial effects, the beneficial implications of digital media uses for adolescents’ sexuality. Moreover, attention needs to be paid to underlying processes explaining the overall sexual socialization of adolescents via the uses of sexually oriented digital media. Lastly, the bidirectional nature of the link between such media uses and sexual outcomes needs to be further explored.

7 Culture and Digital Media in Adolescent Development

Adriana M. Manago and Jessica McKenzie

Digital media are integrated into the lives of adolescents in almost every corner of the globe, yet the extent of integration, how media are used, and the effects of media in development are anything but universal. Much of what is known about adolescent digital media use and its consequences center on high-income economies – particularly in the USA and Western Europe (e.g., Twenge et al., Reference Twenge, Martin and Spitzberg2019; Vanden Abeele, Reference Vanden Abeele2016). Comparatively less is known about media use in lower- and middle-income economies, where digital media use has risen exponentially – especially among youth – in a short period of time (Silver et al., Reference Silver, Smith, Johnson, Jiang, Anderson and Rainie2019). Between 2000 and 2022, internet growth rates in Africa, Asia, Latin America/Caribbean, and the Middle East ranged from 2,300% to 13,000%, compared to 200–600% internet growth rates in Europe, North America, and Oceana/Australia during the same period of time (Internet Usage Statistics, 2022). Indeed, the increase in digital media use is now led by emerging and developing world regions (Poushter et al., Reference Poushter, Bishop and Chwe2018).

The international perspective on digital media and adolescent development we provide in this chapter is important for a number of reasons. First, international perspectives help Western-based developmental psychologists such as ourselves appreciate human diversity and understand our own WEIRD (Western, Educated, Individualistic, Rich, Democratic; Henrich et al., Reference Henrich, Heine and Norenzayan2010) perspectives on technology and human development. Second, cross-cultural research helps us to see how digital media such as mobile devices and social media platforms are cultural tools in the sociocultural tradition of Lev Vygotsky, rather than separate, disconnected, “virtual” places. Cultural tools are material and symbolic resources that accumulate through social processes across generations and that mediate human thinking and action (Cole & Scribner, Reference Cole, Scribner, Cole, Scribner, John-Steiner and Souberman1978). Tools enable children to master psychological functions like memory, attention, and interpretation, which become implicated in a culture’s definition of intelligence (Maynard et al., Reference Maynard, Subrahmanyam, Greenfield, Sternberg and Preiss2005). Although Vygotsky’s theory is generally applied to cognitive development, the idea that digital media are cultural tools transforming human activity and psychological functioning can also be applied to social skills and identity development during the transition to adulthood (Manago et al., Reference Manago, Graham, Greenfield and Salimkhan2008).

In conceptualizing digital media as cultural tools, we can examine the affordances or “opportunities for action” they offer, which are materially and socially constituted (Hutchby, Reference Hutchby2001). That is to say, the design of a social media platform or mobile device suggests to users how the technology should be used, but at the same time, these tools may be employed by communities in ways designers may have never imagined (Kling, Reference Kling2007). Cultural beliefs, values, and institutions influence how and for what purpose adolescents use digital media, and thus the psychological outcomes of use. A relational perspective on affordances suggests that the design of digital tools structure (constrain and enable) certain actions (e.g., one-to-many communication) but have differing ramifications for psychological development depending on social constructions of digital media use (e.g., what is communicated). Furthermore, cultural tools are transformative in the process of mediation and adolescents are uniquely positioned in societies to be brokers of cultural change across generations (Manago et al., Reference Manago, Santer, Barsigian, Walsh and McLean2022). In short, we view youth as active participants in their socialization, and in cultural evolution more broadly, through their use of digital media to negotiate their everyday social lives.

In this chapter, we present cultural perspectives on adolescent development and digital media deriving from international research. Although our focus is international, many of the issues we touch upon can be applied to variability within multicultural societies such as the USA. In keeping with our transactional view, we explore how shared values, structures of community, and notions of selfhood shape, and are shaped by, digital media use. To balance the disproportionate representation of survey research with samples in North America and Western Europe, we looked to anthropological and ethnographic research, including our own fieldwork in Thailand (McKenzie) and a Maya community in Mexico (Manago).

Cultural Values and Digital Media Use around the World

Research suggests that digital communication technologies promote individualistic values and mobility, individual expression, and stimulation (Hansen et al., Reference Hansen, Postmes, Tovote and Bos2014; Manago & Pacheco, Reference Manago, Pacheco and McKenzie2019; Pathak-Shelat & DeShano, Reference Pathak-Shelat and DeShano2014). But to what extent do such values displace collectivistic values and traditional models of interpersonal relationships – particularly in emerging and developing world regions, where values of collectivism, age-based hierarchy, and family obligation dominate? In the paragraphs that follow, we discuss how digital media are used and the effects of digital media in world regions experiencing a rapid rise in internet and social media use. We focus on how cultural values shape adolescent digital media use, and on how adolescents reshape cultural values through their digital media use. We also consider the implications of this digital media-inspired cultural value reshaping on adolescent well-being.

Africa

Quantitative research in Nigeria suggests that social media reshapes core values of respect for old age, traditional ways of dress, and language use (Asemah et al., Reference Asemah, Ekhareafo and Olaniran2013). The authors argue that Facebook, Twitter, and 2go are “potent tools of cultural imperialism” (Asemah et al., Reference Asemah, Ekhareafo and Olaniran2013, p. 67), for they encourage Nigerian youth to pattern their lives after foreign culture and drive the loss of traditional values. Yet the authors also highlight the potential utility of these social media in promoting traditional Nigerian values among youth. Certainly, digital media are powerful tools of globalization insofar as they reduce the distance between practices, values, and people from geographically distant world regions. Yet digital media may also encourage localization (the counterforce of globalization) by encouraging the maintenance and even expansion of local values and practices (Hermans & Dimaggio, Reference Hermans and Dimaggio2007) in rapidly changing cultural contexts.

In Ethiopia, Hansen and colleagues’ quantitative work points to continuity and change in cultural values with the experimental introduction of laptops. In one study, Hansen et al. (Reference Hansen, Postmes, van der Vinne and van Thiel2012) found that after one year of laptop use, adolescents more strongly endorsed individualistic values, yet there was no reduction in collectivistic value endorsement. In another study, Hansen et al. (Reference Hansen, Postmes, Tovote and Bos2014) found that children and adolescents – particularly in rural regions – who were given laptops became significantly more positive about gender equality over time than those without laptops. Those with laptops also endorsed other “modern” cultural values (e.g., achievement, self-direction, universalism). Interestingly, though, they found that traditional values (i.e., religion, family) were also strengthened by the introduction of laptops. The effects of internet and social media use were not assessed in these studies because the laptops given were not connected to the Internet, but it is telling that even the use of offline laptops alter the cultural values endorsed by Ethiopian youth.

Asia

Although digital media are marketed as giving youth power and agency, Pathak-Shelat and DeShano’s (Reference Pathak-Shelat and DeShano2014) qualitative research illustrates that traditional Indian values of obedience to elders are also reinforced by rural Indian adolescents’ internalization of parental moral panic about media as risky. They do so by modeling their digital media use around parental concerns ranging from interacting with strangers to developing cancer from new media technologies. Yet adolescents also subtly (re)negotiate age-based hierarchies and power by, for instance, friending those with whom they are unfamiliar (engaging in “risky” behavior) and not friending distant relatives (not respecting familial ties). Importantly, adolescents in this rural Indian context experience media as peripheral rather than central to their lives. Rural Indian youth have less access to mobile phones and computers with internet access (Pathak-Shelat & DeShano, Reference Pathak-Shelat and DeShano2014); they also use digital media in ways that are distinct from youth in urban India – where media use reshapes adolescent cultural practices such as clothing and music choices (Rao et al., Reference Rao, Berry, Gonsalves, Hastak, Shah and Roeser2013).

Research in Thailand, too, points to gaps in media use across rural and urban contexts. The second author’s mixed-methods study found that urban-dwelling Thais spend more time on digital media than rural-dwelling Thais, and that adolescents spend more time on digital media than their parents (McKenzie et al., Reference McKenzie, Castellón, Willis-Grossmann, Landeros, Rooney and Stewart2022). The media-based opportunities and challenges experienced across generation and geographic location speak to continuity in cultural values. Rural and urban adolescents and parents alike perceive connecting with proximal others (e.g., friends, children) as a key technological affordance. That urban adolescents – who spend the most time on digital media – emphasize collectivistic goals illustrates that media are used in ways that align with and promote traditional cultural values. Yet the media-based challenges highlighted point to digital media paradoxes among those who spend the most time online. Urban adolescents simultaneously experience social media as expanding their presence in the world and restricting real-world experiences, and as enabling connections with, and fostering rejection from, friends. Their parents experience media both as tools for achieving closeness with their children and as endangering family bonds by cheapening time spent together.

In urban Thailand, qualitative research indicates that adolescents’ media expertise renders them cultural brokers for their parents (McKenzie et al., Reference McKenzie2019). Adolescents in this society traditionally marked by deference to elders train their parents to use digital technologies, which reshapes traditional power dynamics and hierarchical family relationships. It is noteworthy, though, that parents reassert their position of authority (e.g., by mobilizing their children’s technological desires as opportunities to teach culturally salient lessons about necessity) and that adolescents use their digital media expertise to assist and serve their parents. This points to continuity of Thai values for age-based hierarchy, moderation, and filial piety, even in the face of rapid technological change.

Latin America/Caribbean

The work of Ferguson and colleagues highlights the influence of digital media on adolescent values and identity in Jamaica. Across two studies, they found that roughly one-third of urban Jamaican adolescents were remotely acculturated to American culture (Ferguson & Bornstein, Reference Ferguson and Bornstein2012, Reference Ferguson and Bornstein2015). One key avenue through which this remote acculturation occurs is indirect intercultural contact with the United States via media – including social media. Their quantitative research indicates that, compared to their “traditional Jamaican” counterparts, “Americanized Jamaican” adolescents are more affiliated with European American identity, hold weaker beliefs about family obligations, and experience greater conflict with their parents.

The first author’s mixed-methods research with young adults in a Maya community in Mexico indicates that cultural values shape how young people think about the benefits and risks of information communication technologies (ICTs) (Manago & Pacheco, Reference Manago, Pacheco and McKenzie2019). Examining indigenous beliefs about ICTs shortly after the installation of a communication tower, the study found that a commonly discussed ICT benefit was enabling frequent family communication and family closeness and that a commonly discussed ICT risk was their danger in drawing attention away from the family. That ICTs are perceived as promoting and hindering family relationships underscores the role of traditional, collectivistic values in shaping youth perspectives of digital media. ICT benefits also highlighted – particularly among those with higher educational attainment – values of stimulation and self-expression, which involve seeking new information and exploring outside of traditional community structures. Here we see the influence of exposure to Western values of individualism, which are spread via ICTs.

Middle East

For each of the preceding world regions discussed, it was possible to highlight research on adolescent media use and values in nations experiencing dramatic digital media expansion. Though the Middle East includes high-income nations with relatively long-standing digital media integration and low-income nations with dramatic digital media expansion in recent years (Internet Usage in the Middle East, 2022; World Bank Country and Lending Groups, n.d.), most relevant research focuses on the former. Mixed-methods research in high-income Israel, however, points to the role of digital media in reshaping cultural values and family relationships.

Abu Aleon et al. (Reference Abu Aleon, Weinstock, Manago and Greenfield2019) assessed values among three generations of Bedouins with vignettes that involved a disagreement between two characters: one that endorsed traditional values (family obligation, interdependence, and gender hierarchy) and other that endorsed modern Western values (individual achievement, independence, and gender equality). They found that younger generations of Bedouins were more likely to endorse gender equality than were older generations, and that females were a generation ahead of males in endorsing gender equality and independence. Importantly, time spent on the Internet and watching television were identified as “motors of change” toward Western value endorsement. Mesch’s (Reference Mesch2006) quantitative examination of Israeli adolescent internet use points to how adolescent internet use affects family cohesion. They found that the more time adolescents spent online, the less time they spent with their parents, and that the purpose of adolescent internet use mattered where family conflict is concerned. While adolescent internet use for social purposes was positively associated with intergenerational family conflict, internet use for educational purposes was not.

Summary

Cultural values influence how digital media are used and the effects of digital media. On the one hand, adolescents use and perceive digital media in ways that align with cultural values. On the other hand, adolescent digital media use reshapes cultural values and interpersonal relationships. The research discussed also illustrates how risks and opportunities of digital media are customized by developmental period, generational cohort, and cultural context. In emerging and developing world regions where technological change is particularly rapid, risks include the potential loss of traditional cultural values and an emergent cultural gap between adolescents and parents. Opportunities include emergent adolescent agency in shaping their development and in reshaping cultural values deemed incongruent with their lived 21st-century realities.

Considering adolescent well-being as it intersects with cultural values, digital media may act as a double-edged sword. The psychological task of encountering and reconciling diverse value systems likely facilitates adolescent perspective-taking and the development of multifaceted, dynamic thinking that is adaptive in our multifaceted and dynamic world. Yet the task of coherently integrating local and global value systems likely presents unique challenges for adolescents (McKenzie, Reference McKenzie2020). The difficulties associated with knitting together potentially incompatible value systems and identities may render adolescents more at risk of developing bifurcated or differentiated selves (McKenzie, Reference McKenzie2019), thereby threatening the development of an integrated self – a key task of adolescence (Erikson, Reference Erikson1963).

The effects of digital media in world regions experiencing rapid technological growth in some ways mirror the effects in the culturally diverse USA. As explained in this section, adolescent digital media use reshapes cultural values and parent–child power dynamics in India and Thailand. Among rural teenage girls in the Midwestern USA, especially rural girls of color, social media is used to gain and assert power and control, which is perceived as lacking in their offline lives (Rickman, Reference Rickman2018). Like Thai adolescents who act as media-based cultural brokers for their parents, lower-socioeconomic-status American youth frequently assist their parents with technology (Rideout & Katz, Reference Rideout and Katz2016) – likely renegotiating parent–child power dynamics in the process. Also highlighted in this section is that adolescent media use does not completely unroot traditional cultural values. From Nigeria and Ethiopia to India, Thailand, and Mexico, media are avenues for localization and local value reassertion. This aligns with Latino families in the USA, who often use digital media in ways that center collectivism (e.g., collaborative father–son searches, sister and brother producing media together) (Levinson & Barron, Reference Levinson and Barron2018). Research explicitly addressing the overlapping consequences between and within cultural communities would sharpen the cultural study of adolescent digital media use.

Structures of Community: The Nature of Social Ties in Digital Societies

Many sociological theorists have pondered questions about the impacts of digital communication on structures of community and the ways in which social relations are organized (Castells, Reference Castells1996; Rainie & Wellman, Reference Rainie and Wellman2012). One perspective is that communication technologies, particularly social network sites, have greatly reduced the time costs of maintaining relationships such that youth today have more opportunities to interact with larger swaths of diverse others than was possible in the past (Manago & Vaughn, Reference Manago, Vaughn and Demir2015). Yet, much of our thinking on this issue is grounded in evidence from WEIRD samples and Western philosophical traditions. In this section, we interrogate a common framework for understanding social ties in digital societies and present alternative possibilities that may better account for the impact social media is having on the organization of adolescents’ social relations.

The Mobility Narrative

Western theories regarding the consequences of communication technologies for human social relations often reflect a mobility narrative (Hampton, Reference Hampton2016). In this narrative, industrialization, transportation systems, urbanization, and communication technologies have brought about greater migration, occupational specialization, and shifts in social structures away from permanent, tight-knit groups grounded in shared geography, to impermanent, heterogenous, and expansive person-centered networks spread across various contexts (e.g., Greenfield, Reference Greenfield2009; Rainie & Wellman, Reference Rainie and Wellman2012). Mobile devices and social media amplify historical trends toward increasing individual mobility by introducing new affordances into social life such as communication at a distance and asynchronous one-to-many (masspersonal) communication that allow people to transcend the limitations of time and space to construct looser networks of associations (Donath, Reference Donath2008; Wellman, Reference Wellman, Tanabe, van den Besselaar and Ishida2002). Digital communication technologies also extend individuals’ capacities to connect through shared personal interests, rather than ascribed relationships such as kinship, and to overcome the constraints of social bonds while still deriving social resources from them (Rainie & Wellman, Reference Rainie and Wellman2012).

The mobility narrative is useful for explaining certain patterns in the international social media research literature. In the West, Facebook has facilitated more extensive webs of associations with social resources for personal exploration and self-expression (Brandtzaeg, Reference Brandtzaeg2012; Ito et al., Reference Ito, Baumer and Bittanti2009; Manago et al., Reference Manago, Taylor and Greenfield2012). Bridging social capital resources such as nonredundant information and novel perspectives are more abundant in social structures with many weak ties (Granovetter, Reference Granovetter1973; Williams, Reference Williams2006) and studies have shown positive associations between social media use, network size, and bridging social capital among US college students (Ellison et al., Reference Ellison, Steinfield and Lampe2007; Mariek et al., Reference Mariek, Vanden and Marjolijn2018), adolescents in Australia (J. Y. Lee et al., Reference Lee, Park, Na and Kim2016), and early adolescents in the Netherlands (Antheunis et al., Reference Antheunis, Schouten and Krahmer2016). Similar associations have also been found outside the West, among university students in Karachi, Pakistan (Raza et al., Reference Raza, Qazi and Umer2017), South Africa (Johnston et al., Reference Johnston, Tanner, Lalla and Kawalski2013), Beijing, China (Liu et al., Reference Liu, Shi, Liu and Sheng2013), and adolescents in South Korea (J. Y. Lee et al., Reference Lee, Park, Na and Kim2016). In Manago’s field site in the Maya community of Zinacantán, emerging adults who began using the Internet after a communication tower was installed in 2010 constructed social networks through a paper and pencil mapping activity (Antonucci, Reference Antonucci1986) comprising greater proportions of nonpermanent social connections (nonkin) compared to emerging adults who did not have access to the Internet (Manago & Pacheco, Reference Manago, Pacheco and McKenzie2019).

Nevertheless, some patterns in the research literature are not well understood through a mobility narrative. The degree to which young people use social media to build large networks of bridging social capital varies around the world and depends on other relational structures in their cultural contexts. Research has shown that in social contexts outside the USA where it is less normative to sever old ties and form new ones such as in France (Brown & Michinov, Reference Brown and Michinov2017), Japan (Thomson et al., Reference Thomson, Yuki and Ito2015), South Korea (Cho, Reference Cho2010), and among Palestinians in Israel (Abbas & Mesch, Reference Abbas and Mesch2015), adolescents and emerging adults tend to use social media to construct smaller and more intimate networks based on their face-to-face relationships. The problem of “context collapse” identified in the West as the mixing of multiple, distinct, and even unknown audiences on social network sites leading to the disintegration of contextual cues for self-presentation (boyd, Reference boyd2008; Vitak, Reference Vitak2012) is a nonissue in southeast Turkey, where people use Facebook to construct multiple closed groups for social interaction and make extensive use of the private chat feature (Costa, Reference Costa2018). Even adolescents and emerging adults in the USA and UK who construct large online networks tend to use social network sites to maintain connections with existing face-to-face contacts, rather than to meet new people and expand social horizons (Livingstone & Sefton-Green, Reference Livingston and Sefton-Green2016; Manago et al., Reference Manago, Taylor and Greenfield2012; Subrahmanyam et al., Reference Subrahmanyam, Reich, Waechter and Espinoza2008).

Additionally, a networked structure of social ties is not endemic to the design of social media. On Renren in China and Cyworld and Kakaostory in Korea, relationships are organized in closed structures of concentric circles, and norms of reciprocity and mutual obligations are central to activities on the site (Hjorth, Reference Hjorth2010; J. Y. Lee et al., Reference Lee, Park, Na and Kim2016; Li & Chen, Reference Li and Chen2014). As social media continue to evolve and proliferate, youth are increasingly alternating between different platforms and tools to manage different kinds of relationships in an integrated environment of affordances, what Madianou and Miller (Reference Madianou and Miller2013) call “polymedia” environments. For example, US college students use Twitter and Instagram to generate bridging social capital, Facebook for family, and Snapchat to increase intimacy with close others (Phua et al., Reference Phua, Jin and Kim2017; Shane-Simpson et al., Reference Shane-Simpson, Manago, Gaggi and Gillespie-Lynch2018) while Chinese international students use Facebook as a tool for generating bridging capital and Renren for maintaining connections to home life (Li & Chen, Reference Li and Chen2014).

Alternatives to a Mobility Narrative

Hampton (Reference Hampton2016) proposes that meta-modernity is a better narrative metaphor for understanding social media and social structures in contemporary times. In his view, both individual mobility and social accountability are becoming amplified with social media. Communicative affordances for persistent contact and pervasive awareness are reinstating some preindustrial relational structures that counteract growing individual mobility. Mobile devices and social media amplify social obligations and commitments, making people constantly accessible in the present and to people of the past, connecting us more permanently across lifespans and generations. Indeed, studies with adolescents in Europe (Mascheroni & Vincent, Reference Mascheroni and Vincent2016) and college students in the USA (Hall & Baym, Reference Hall and Baym2012) exemplify how mobile phones have increased norms for perpetual communication, creating new pressures that promote dependence and satisfaction with close others but also feelings of overdependence and dissatisfaction. In addition, one-to-many forms of asynchronous communication via status updates on social media supply everlasting streams of social information that persist and scale (boyd, Reference boyd and Papacharissi2010), recreating the passive informal watchfulness of small, tight-knit communities where the audience is ambiguous and the watchers are also being watched (Hampton, Reference Hampton2016; Marwick, Reference Marwick2012). Pervasive awareness can be found in the ways youth often exhibit heightened conformity to community expectations for gender in their photographs on social media, whether those expectations involve carefully curating sexually attractive selfies in the south of Italy (Nicolescu, Reference Nicolescu2016) or upholding modesty and family honor in New Delhi (Mishra & Basu, Reference Mishra and Basu2014), rural China (McDonald, Reference McDonald2016), and southeast Turkey (Costa, Reference Costa2016).

Another theoretical perspective is scalable sociality, posited by Miller and colleagues (Reference Miller, Sinanan and Wang2016) in a multivolume series of ethnographies on social media use in southeast Turkey, south Italy, northern Chile, south India, rural China, industrial China, emergent Brazil, an English village, and Trinidad. These authors argue that social media have “colonized a space of group sociality between the private and the public” (Miller et al., Reference Miller, Sinanan and Wang2016, p. 286), introducing new structures of relations and genres of communication at various points on continuums from small groups and intimacy to large groups and publicness. To illustrate, Miller et al. show how 11- to 18-year-olds in the English village use dyadic mobile phone messages to talk to their best friends, Snapchat to develop trust in small groups, WhatsApp to communicate with classmates (often same-sex groups discussing other-sex classmates), Twitter to engage in school-wide banter, Facebook to interact with groups outside school such as family, neighbors, and workmates, and Instagram to entertain strangers with visual images. Sociality can also be scaled within a single platform through various functionalities (e.g., use of privacy settings) or through communication strategies themselves (e.g., social steganography – embedding private, hidden messages in public communication, Marwick & boyd, Reference Marwick and boyd2014). Connecting the idea of scalable sociality back to cultural tools, we can see how social media would extend adolescents’ capacities to develop skills for social relations at various scales of interaction.

Importantly, the consequences of social media for adolescent development depend on what is being scaled relative to youths’ social contexts. Just as Facebook in the USA and QQ in China have scaled public broadcasting (e.g., TV, newspapers, radio) down to individuals contributing to large groups, WhatsApp in Latin America and WeChat in China have scaled intimacy up from face-to-face interactions and the telephone (Miller et al., Reference Miller, Sinanan and Wang2016). In some cases, mobile devices and social media have intensified intimacy by creating new genres of intimate romantic relations, particularly in cultures with greater family mediation in romantic partnering such as south India (Venkatraman, Reference Venkatraman2016), rural China (McDonald, Reference McDonald2016), Muslim southeast Turkey (Costa, Reference Costa2016), and in Zinacantán, Mexico (de Leon-Pasquel, Reference de León-Pasquel2018). Intimacy and mobility can also be scaled together as is the case with social media facilitating reinforcement of emotional bonds in cross-national families in Trinidad (Sinanan, Reference Sinanan2017), allowing families to stay connected when miners are absent for long periods of time in Chile (Haynes, Reference Haynes2016), and helping migratory industrial workers maintain stable connections in industrial China (Wang, Reference Wang2016). Social media also introduce new opportunities to reinforce and scale traditional social structures, such as in south India where symbolic kinship structures of extended families in caste traditions have become a metaphor for how youth arrange contacts on social media (Venkatraman, Reference Venkatraman2016). Counter to the linear direction of cultural change toward individualism in the mobility narrative, new scales of sociality may have unexpected consequences for psychological development. For example, Miller and colleagues (Reference Miller, Sinanan and Wang2016) found a new kind of openness to strangers with the introduction of social media in rural China but wariness of strangers through social media in Italy and England.

Summary

A mobility narrative may not be comprehensive enough to capture the multifaceted structures of community that are evolving with the spread of digital tools. Digital tools afford customizable sociality and mobility, but also introduce new kinds of communities, as well as social pressures and constraints at different scales of interaction. Moreover, the ramifications of digital media for adolescent development depend on what is being scaled relative to youths’ everyday lives, which is quite different across the globe and across groups in a multicultural society such as the USA. For example, social media create new opportunities for community and critical consciousness raising among racial and ethnic minority youth (Tynes et al., Reference Tynes, Garcia, Giang and Coleman2011) but also new capacities for racial and ethnic discrimination to occur (Lozada et al., Reference Lozada, Seaton, Williams and Tynes2021). For LGBTQ+ youth, social media are new avenues for intimacy (Marston, Reference Marston2019) and also public visibility (Rubin & McClelland, Reference Rubin and McClelland2015). As adolescents negotiate risks and opportunities at various scales of sociality they are learning new kinds of social skills adaptive for digitally mediated societies that contribute to their identity development and well-being.

The Culturally and Digitally Mediated Self

Western ideals and notions of personhood tend to dominate developmental science on the digitally mediated self. Optimistically, we see interactive media as offering enhanced opportunities for exploration, expression, reflection, and curation in the process of self-construction in the transition to adulthood (Ito et al., Reference Ito, Baumer and Bittanti2009; Manago et al., Reference Manago, Graham, Greenfield and Salimkhan2008). A more pessimistic view blames social media for narcissism in the USA and greater preoccupation with superficiality and external validation among young people (Twenge, Reference Twenge2013). As we hope to make clear in the following paragraphs, these opportunities and risks for self-development are not functions of digital tools themselves; instead, they reflect social constructions of digital media use, including hopes, fears, and expectations for how the self should be represented. In this section, we compare Western-based norms and meanings for digital self-presentation with those outside the West. This comparison will call into question universal claims about the impact of digital media on self-development and highlight how digital tools are used for both cultural reproduction and transformation.

Is Social Media an Identity Playground?

The popular New Yorker cartoon published in 1993, “on the internet nobody knows you’re a dog,” cleverly illustrates early perspectives in the USA regarding the Internet’s impact on identity. Research at this time suggested that the disembodied nature of computer-mediated communication (i.e., reduced social cues, asynchrony, and geographical distance) would facilitate anonymity, pretense, exploration, and transcendence of offline limitations in self-presentations (McKenna & Bargh, Reference McKenna and Bargh2000; Rodino, Reference Rodino1997; Turkle, Reference Turkle1997). But as the social media landscape evolved, becoming more visual and less anonymous (e.g., Facebook’s real-name policy), concerns shifted to adolescents’ self-disclosures and risks to their personal privacy (Livingstone, Reference Livingstone2008; Tufekci, Reference Tufekci2008). Research also began to emphasize how authenticity in combination with positive curation in online self-presentations generated audience support and greater self-regard (Marwick & boyd, Reference Marwick and boyd2011; Yang & Brown, Reference Yang and Brown2016). Other studies, both in the USA and Europe, showed that the presentation of false selves online was an indication of lower degrees of identity synthesis (Michikyan et al., Reference Michikyan, Dennis and Subrahmanyam2015) or a response to offline dysfunction, occurring at higher rates among lonely adolescents (Valkenburg & Peter, Reference Valkenburg and Peter2008) and those with poor social skills and social anxiety (Harman et al., Reference Harman, Hansen, Cochran and Lindsey2005).

Ethnographic approaches during this period of time documented more nuanced combinations of authenticity and experimentation happening among adolescents online. Fieldwork in the USA (boyd, Reference boyd2014) and the UK (Livingstone & Sefton-Green, Reference Livingston and Sefton-Green2016) captured the polymedia nature of adolescents’ lived experiences with social media and how they were learning to express different sides of themselves depending on affordances for visibility and privacy in various social milieus. For example, Livingstone and Sefton-Green described how teenagers in a London secondary school used Tumblr to explore emerging facets of the self anonymously while using Facebook to perform “civil” selves that conformed to expectations of the school community and that engendered shallow social acceptance. Case studies of adolescents in the USA have also depicted different genres or conventions of participation across platforms including “hanging out,” “messing around,” and “geeking out” – the latter of which involves in-depth identity exploration of niche interests (Ito et al., Reference Ito, Baumer and Bittanti2009).

A different story of the digitally mediated self has emerged in South Korea and Japan. In South Korea, the first country in the world where widespread use of a social networking site (Cyworld) occurred among youth, digital self-presentation has not been about the exploration of new horizons but about the mundane (Hjorth, Reference Hjorth2007; D. Lee, Reference Lee2010). In her ethnographic research with university students in Korea in the early 2000s, Hjorth found Cyworld was used to create reels of everyday lived content that could be shared, mimicking the gift-giving in Korean culture that reinforces social ties. Youth used digital tools to capture the ordinary and thus deeply personal aspects of themselves to overcome, rather than exploit, the lack of social presence in computer-mediated communication. This finding is similar to studies with Japanese youth at this time who used early forms of social media to foster a new kind of co-presence and shared perspective of daily life, akin to the intimacy of being together without having to say anything (Ito & Okabe, Reference Ito, Matsuda and Okabe2005). The greater emphasis on tethering in these studies is also present in the language for digital tools; in contrast to the term “mobile phone” in the West that means a device that travels, the term “keitai” in Japan signifies a device connected to the body as an appendage, the emphasis on attachment (Ito et al., Reference Ito, Matsuda and Okabe2005). Similarly in Singapore, the term is “hand phone,” suggesting alternative social constructions of self in relation to digital media that may reflect broad differences between Eastern and Western cultures.

A cross-cultural perspective on mobile devices and social media reveals how these tools are not generally used to escape social norms for self-representation but to conform to them. Kim and Papacharissi (Reference Kim and Papacharissi2003) analyzed US and Korean Yahoo! Geocities home pages and found US virtual actors were more likely to present themselves with text-based communication and to describe themselves directly (i.e., stating personality traits) – a reflection of low-context communication in which independent selves transmit explicit and direct messages that can be separated from the context without loss of meaning. Korean virtual actors tended to communicate their identities through more ambiguous multimedia imagery – a reflection of high-context communication where interdependent selves are less direct and more implicit, relying on contextual factors to transmit meaning (see also Gudykunst et al., Reference Gudykunst, Matsumoto, Ting-Toomey, Nishida, Kim and Heyman1996). A departure from individualistic self-presentation norms of the USA has also been documented more recently in Turkey. Comparing Turkish and American adolescents’ self-presentations on Facebook, researchers found adolescents in the USA were more likely to use promotion strategies in their self-representations, conforming to ideals for exalting the self, while those in Turkey tended to conform to Turkish ideals by presenting themselves through exemplification strategies that demonstrated their moral principles (Boz et al., Reference Boz, Uhls and Greenfield2016).

Conformity to gender norms on social media is widely observed in the research literature and further demonstrates how cultural expectations for self-presentation are projected to screens. Some researchers have interpreted consistent international gender differences in Facebook profiles (e.g., men present objects to convey status, women present family photos) as due to biology and natural selection (e.g., Tifferet & Vilanai-Yavets, Reference Tifferet and Vilnai-Yavetz2014). However, this interpretation fails to recognize historical formations of the patriarchal arc that has spread east and west from the invention of the plow in the Middle East, and through colonization, shaping hierarchical gender relations in particular ways (Quinn, Reference Quinn, Mathews and Manago2019). Within the patriarchal arc there are also cultural differences in gender that are translated into digitally mediated contexts. One content analysis comparing photos of US and Chinese athletes at the 2016 Rio Olympics on Twitter and Sina Weibo found that Chinese female athletes were more likely to incorporate smiling and a tilted head position compared to their Chinese male counterparts; US female athletes were more likely to depict themselves posed on a knee or body arched compared to their male counterparts, who tended to post photos of themselves upright (Xu & Armstrong, Reference Xu and Armstrong2019). This study also found evidence of greater egalitarianism in US photos compared to Chinese photos, which could be due to the ongoing influence of Confucian ideals for male dominance in China and the attenuating effects of Title IX on male dominance in US sports.

Research on gender self-presentation via digital media reveals cultural continuity but also cultural change. Studies on Facebook use in Muslim cultural contexts show how young women resist traditional constraints such as sexual purity and responsibility for family reputation by segmenting their audiences on social media (Al-Saggaf, Reference Al-Saggaf2011; Shen & Khalifa, Reference Shen and Khalifa2010). One interview study with Muslim university students in New Delhi found that young women negotiated multiple audiences on Facebook, presenting themselves as “nice” and virtuous to uphold their family’s honor but also using privacy settings to restrict surveillance and judgment from more conservative parts of their kin networks and express themselves outside traditional norms (Mishra & Basu, Reference Mishra and Basu2014). Similarly, ethnographic research in South Korea describes how young women presented themselves on their Cyworld mini-homepages to achieve conventional patriarchal definitions of submissive female beauty in South Korea; yet, in the process of framing, editing, manipulating, and curating their images, the young women also took control of the gaze, which opened up new experiences of power in their identity development (D. Lee, Reference Lee2005).

Is the Selfie Narcissistic?

A variety of studies have found associations between narcissistic personality traits and social media use, mostly among college students on Facebook but also among social media users in China, Japan, Europe, Australia, and Russia (see meta-analysis by McCain & Campbell, Reference McCain and Campbell2018). The assumption in the framing and interpretation of these studies is that posting photos and status updates on social media is ultimately self-promotional and therefore cultivates an unrealistic, self-serving, entitled, and inflated sense of self as special and unique (Gentile et al., Reference Gentile, Twenge, Freeman and Campbell2012). The so-called selfie, a photo taken by the self of the self, has been an emblem of this assumption, construed as an indication of vanity and often employed to accuse young women of self-indulgence, triviality, and attention-seeking (Burns, Reference Burns2015).

International research has brought to light the existence of alternative paradigms surrounding the selfie that likely have very different consequences for youth self-development. In a favela of Brazil, adolescents post selfies on Facebook to reflect on themselves and the violence in their neighborhoods and to send a signal to their parents (who regularly check the site) that they are safe as they navigate daily life (Nemer & Freeman, Reference Nemer and Freeman2015). Selfies in this context are not fostering narcissism but instead helping youth contest the power and surveillance of local drug lords. The banal self-portraits that Japanese and South Korean youth exchanged in early iterations of social media created ongoing togetherness in daily life, not inflated sense of selves (Hjorth, Reference Hjorth2007; Ito & Okabe, Reference Ito, Matsuda and Okabe2005; D. Lee, Reference Lee2010). However, relational selfies are not just found outside of WEIRD contexts. American and British university students also use selfies relationally when they exchange unedited and disappearing images on Snapchat as a form of intimate conversation; as one participant in a study said, “I’ve literally had a ten-minute conversation with my friend just doing facial expressions” (Katz & Crocker, Reference Katz and Crocker2015, p. 1869). Katz and Crocker report that Chinese university students used WeChat in similar ways but instead of facial expressions, animations symbolizing emotions and actions were used to maintain visual conversations. It is impossible to disentangle whether this observed difference is due to the alternative affordances via Snapchat versus WeChat or due to cultural differences in ideals for emotional expression (see Tsai, Reference Tsai2017).

Understandably, selfies in more public social media contexts tend to involve greater deliberation and curation. But does crafting favorable social impressions through selfies in more public contexts equate to an unrealistic and inflated sense of self? In their compilation of international ethnographies, Miller and colleagues (Reference Miller, Sinanan and Wang2016) show that, indeed, posting idealized versions of the self occurred throughout their field sites; yet what the ideal looks like and what it means to people vary widely. While young factory workers in industrial China posted aspirational photos of economic wealth and consumption on the platform QQ, their counterparts in rural China posted photos of family life that combined collectivistic (gratitude to elders) and individualistic (romantic love) aspirations. Selfies among evangelicals in their Brazilian field site showcased material wealth to signify one’s religiosity, and in Trinidad, to demonstrate the virtue of hard work. Sometimes the ideal self, such as those presented in selfies in Chile and in an English village, was about demonstrating authenticity through conformity to the ordinary. The authors describe the “footsie” version of the selfie that was popular in Chile where photographers take photos of their feet in a lounging position watching television or playing video games. The footsie is curated to communicate authenticity out of casualness. The footsie would not travel well to Chinese selfie celebrity culture, where photoshopping is an expected and normative courtesy such that not using software editing applications to enhance one’s images and those of one’s friends is considered impolite (see Fan, Reference Fan2017).

Summary

This section illustrates that social media are not generally used to escape norms and construct an inflated sense of self but instead, to construct a self in line with cultural norms and ideals. Universal claims about the impacts of digital media on adolescent self-development are problematic because norms and ideals for online self-presentation differ across cultures. In a multicultural society such as the USA, race, class, and gender shape how adolescents present themselves through social media and how those self-presentations are interpreted and evaluated (e.g., Daniels & Zurbriggen, Reference Daniels and Zurbriggen2016; Kapidzic & Herring, Reference Kapidzic and Herring2015). Senft and Baym (Reference Senft and Baym2015) argue that although selfies are an expression of human agency, they are also “created, displayed, distributed, tracked, and monetized through an assemblage of nonhuman agents” (p. 1589). Once an image is digitized, it takes up space in the “digital superpublic” and persists outside of the context in which it was first produced, shared, and viewed. As adolescents negotiate online self-presentations and make decisions about who they are and how they want to appear, they contribute to the cultural artefacts circulating in their communities.

Challenges and Future Directions in the Study of Culture and Digital Media

Digital media are cultural tools that at once reflect the cultural values and biases of the creators (Manago et al., Reference Manago, Santer, Barsigian, Walsh and McLean2022) and whose use is shaped by the cultural values of the users (McKenzie et al., Reference McKenzie2019). As reflected in this chapter, adolescents and emerging adults – who lead digital and social media use around the world (Pew Research Center, 2019; Silver et al., Reference Silver, Smith, Johnson, Jiang, Anderson and Rainie2019) – also contribute to cultural change through their media use. Given the inherently cultural nature of digital media, foregrounding culture in media studies is critical.

Cultural Challenges

Popular discourse and research articles alike are awash with broad claims about how digital and social media affect teens (e.g., Crone & Konijn, Reference Crone and Konijn2018; Schrobsdorff, Reference Schrobsdorff2016; Twenge, Reference Twenge2017), without adequately attending to how culture shapes media use and its effects. When culture is attended to by media scholars, it is often treated as synonymous with “nation.” This treatment of nations as monolithic cultural entities is problematic for media studies, as significant within-nation heterogeneity exists in media access and use, and in its effects on adolescents (McKenzie et al., Reference McKenzie, Castellón, Willis-Grossmann, Landeros, Rooney and Stewart2022; Sheldon et al., Reference Sheldon, Herzfeldt and Rauschnabel2020). Although within-culture variation sometimes exceeds between-culture variation (Sheldon et al., Reference Sheldon, Herzfeldt and Rauschnabel2020), digital media research that takes culture into account typically ignores variations within cultural groups (Cardon et al., Reference Cardon, Marshall and Choi2009). In our increasingly multicultural world, we must go beyond investigating the influence of national culture to examine the roles of ethnicity, race, religion, generation, and geographic location in adolescent digital media use and its consequences. With globalization, we must also consider processes of remote acculturation that may increasingly apply to European-American youth (i.e., the rising popularity of K-pop in the USA) and the unique perspectives of immigrant and bicultural youth, who are negotiating multiple worldviews across different social media platforms (Bae, Reference Bae and Mazzarella2010; Bae-Dimitriadis, Reference Bae-Dimitriadis2015).

Another cultural challenge is that adolescent digital media use is typically examined in wealthy nations with more established digital media integration. Findings from these populations tend to be interpreted in terms of a universal biologically governed individual, which masks the way that culture is operating in the West. Moreover, a significant gap exists in our understanding of digital media use and its influence on adolescents in poorer nations experiencing a rapid rise in digital media integration. For example, adolescent media use studies in the Middle East overwhelmingly focus on Israel (e.g., Abu Aleon et al., Reference Abu Aleon, Weinstock, Manago and Greenfield2019; Mesch, Reference Mesch2006), a high-income economy with a 451% internet growth rate from 2000 to 2021; far less is known about media use in low-income Yemen (with a staggering 52,592% internet growth rate during that time) and in middle-income Iraq and Iran (with 196,100% and 31,135% internet growth rates during that time, respectively) (Internet Usage in the Middle East, 2021). In nations with a dramatic rise in digital media integration, such as Yemen, Iraq, and Iran, we are likely to see cultural clashes between values promoted by digital media (e.g., individualism, self-expression, and stimulation) and indigenous cultural values. Such clashes, in turn, likely reshape adolescent development, well-being, and intergenerational relationships in these nations in rather profound ways. Alternatively, adolescents in these regions may be using social media as cultural tools to reproduce and reshape culture. These possibilities should be of great interest to adolescent media scholars, as an estimated 84% of the world’s population reside in low- and middle-income countries experiencing a rapid rise in digital media use among youth (Ortiz-Ospina, Reference Ortiz-Ospina2017; Silver et al., Reference Silver, Smith, Johnson, Jiang, Anderson and Rainie2019).

Finally, cultural and cross-cultural adolescent media scholars are tasked with prioritizing youth perspectives. Though helpful, survey-based research typically enters with a priori assumptions about what constitutes risk and opportunity, what identity development looks or should look like, what friendship looks like, what well-being looks like, and so on. But conceptions of risk and opportunity are culturally constructed (Manago & Pacheco, Reference Manago, Pacheco and McKenzie2019; McKenzie et al., Reference McKenzie, Castellón, Willis-Grossmann, Landeros, Rooney and Stewart2022); so too are pathways of identity development (e.g., Sugimura, Reference Sugimura2020), definitions of and meanings ascribed to friendship (e.g., French, Reference French and Jensen2015), and conceptions of well-being (Weisner, Reference Weisner, Ben-Arieh, Casas, Frønes and Korbin2014). Tuning ourselves to the meanings that adolescents themselves ascribe to these concepts is a critical starting point in furthering our understanding of digital media use and its consequences across diverse cultural communities. Doing so will push us to particularize our claims about how digital and social media affect adolescents and ensure that our research aligns with the lived realities of those we aim to represent.

Future Directions

To address the challenges raised above, media scholars must work to understand how culture operates in the lives of adolescents, and how culture structures their digital media use and perspectives of digital media. To be sure, experimental, survey-based, and quantitative approaches to cross-cultural studies of adolescent media offer important insights. The work of Hansen and colleagues, for example, illustrated that cultural panic about the eradication of traditional values with the integration of new media are not entirely founded, as traditional Ethiopian values are not threatened by (Hansen et al., Reference Hansen, Postmes, van der Vinne and van Thiel2012) and even increase with (Hansen et al., Reference Hansen, Postmes, Tovote and Bos2014) the introduction of laptops. This begs an important question, though: Why is this the case? In order to understand the processes whereby cultural values change via, and are maintained through, media use, ethnographic research that foregrounds culture and works to understand how it interacts with digital and social media in the lives of young people will provide invaluable insights.

Cultural foregrounding at each stage of studying adolescent media use – including study design, data collection instruments and procedures, and interpretation – is also essential. This requires that researchers calibrate to, and design measures and materials that are grounded in an understanding of local cultural norms, which may be in flux. Such cultural attunement may require that qualitative data collection methodologies (e.g., interviews, focus groups, social network mapping) be used in place of surveys. This is likely to be especially important when researchers are interacting with marginalized populations in multicultural societies, such as racial, ethnic, sexual, and gender minority youth who may operate on a different set of assumptions from researchers. Such methods are also generally useful for cross-cultural examinations of adolescent digital media use, given the potential for cross-cultural differences in survey response styles to be mistakenly interpreted as cultural differences in the measures being compared (Johnson et al., Reference Johnson, Shavitt, Holbrook, Matsumoto and Van de Vijver2010). This cultural foregrounding is promising in deepening our understanding of the diverse experiences adolescents have with digital media.

Conversely, we must consider how cultural change is inhibited – and adolescent development is controlled – by governments through digital and social media. This is achieved by way of broad-scale internet bans, censorship, and mass surveillance. In North Korea, for example, the government allows only tightly controlled domestic intranet (King, Reference King2019). In Iran, government-issued internet blackouts aim to quell internal unrest and protest (Wolff, Reference Wolff2019). It is also achieved by denying and controlling the use of certain social media platforms. Iran, China, and North Korea have a 100% ban on Facebook (Frenkel, Reference Frenkel2018; King, Reference King2019; Leskin, Reference Leskin2019); China further bans Instagram, WhatsApp, Twitter, Snapchat, Reddit, Pinterest, YouTube, and Google (Leskin, Reference Leskin2019). Finally, it is achieved by using digital media to surveil its citizens and reassert cultural values. Egypt, for instance, has come under international spotlight in recent years for police use of dating apps to locate, imprison, and torture LGBT citizens (AP News, 2020; Culzac, Reference Culzac2014) – thereby limiting sexual expression and exploration and enforcing homophobia. China’s “social credit” system also restricts freedom of expression by using social media surveillance to reassert cultural values of collectivism, conformity, and reputation maintenance (Chen & Zhou, Reference Chen and Zhou2019; Wong & Dobson, Reference Wong and Dobson2019). In 2019, 23 million Chinese citizens were banned from traveling due to poor social credit scores (Reisinger, Reference Reisinger2019). Also in the West, digital algorithms encode and perpetuate racial inequalities (Benjamin, Reference Benjamin2019) while media companies are increasingly exploiting personal data for profit, trading on human behavioral futures in what Zuboff (Reference Zuboff2019) calls “surveillance capitalism.” Each example provided here serves to limit or deny intercultural and intracultural contact, thereby inhibiting cultural change and limiting youth agency over their own development.

We set out in this chapter to explore how digital media are cultural tools in adolescent social development. In bringing together alternative cultural perspectives on digital media use and adolescents’ values, social ties, and self-development, we have begun to shed light on cultural processes in digital media use that often go unacknowledged in developmental psychology research with WEIRD samples. By examining international research and questioning dominant Western paradigms, we hope to inspire more contextual and critical approaches to understanding the effects of social media for adolescent development.

8 Marginalized and Understudied Populations Using Digital Media

Linda Charmaraman , J. Maya Hernandez , and Rachel Hodes

The current generation of adolescents were born into an omnipresent digital world in which offline and online societal and cultural contexts can influence one’s developing sense of belonging and identity. Rapid technological advancements, such as widespread adoption of smartphones, streaming technologies, and online influencers, have changed the way adolescents have been primed and groomed to adapt to the shifting environment. As the field of digital media and social technologies continues to grow, the attention to digital divides becomes less about access to digital technologies and more about how young populations use these technologies in healthy (or unhealthy) ways. By 2013, the vast majority of youth had access to the Internet, including Black (92%), Hispanic (88%), and even youth in low-income neighborhoods (89%; Madden et al., Reference Madden, Lenhart, Duggan, Cortesi and Gasser2013). However, the scholarly reporting of cultural, racial, and economic differences in digital media use typically covers access to the Internet, mobile phones, and favorite social media sites rather than how youth from different marginalized groups actually use technology.

To date, most research has been conducted on White and college samples (Zhang & Leung, Reference Zhang and Leung2014). This further deepens the knowledge gap (or a “second-level digital divide”; Hargittai & Hinnant, Reference Hargittai and Hinnant2008) in understanding how overlooked populations, such as racial-ethnic minorities, sexual and gender minorities, and other vulnerable adolescent populations, may be not only accessing digital media in different ways but also using and repurposing them to subvert the dominant mainstream narratives. Unlike the mainstream media of the 20th century, this socially networked age of the 21st century provides users opportunities to co-construct their identities in the same social and entertainment environments as where they receive their commercial media programming (Manago, Reference Manago, Scott and Kosslyn2015). Since most US-based studies have focused on White or college-based samples to understand social media use (but see Chapter 7 for discussion of cultural differences across the world), there is a silencing of voices that exemplify the diverse identity factors among understudied subgroups of our youth’s digital worlds (Stevens et al., Reference Stevens, Gilliard-Matthews, Dunaev, Woods and Brawner2017). This chapter will discuss the role of digital media on marginalized identity development during adolescence, risk and resilience experiences of social media within these understudied adolescent groups, and challenges and future directions in researching the experiences of these subgroups.

Much like the mainstream televised media messages that dominated past generations, the ever-evolving landscape of digital media is a persistent source of societal messages for adolescents to digest – from unacceptable and acceptable behavior to peer and family relationships to gender and sexual roles to stereotypes and values (Mayhew & Weigle, Reference Mayhew and Weigle2018). Two major developmental tasks for adolescents aged 10–24 are exploring intimacy with others and developing stable personal, social, and collective identities that incorporate gender, racial/ethnic, sexual, moral/religious, and political components (Subrahmanyam et al., Reference Subrahmanyam, Smahel and Greenfield2006). In the sections below, we will explore the role of social media in developing marginalized identities pertaining to race, ethnicity, sexual orientation, homelessness, and disability. Because the emergent development of marginalized identities such as sexual orientation (e.g., Pew Research Center, 2013) or race/ethnicity (e.g., Umaña-Taylor et al., Reference Umaña-Taylor, Quintana and Lee2014) more often developmentally crystalizes in later adolescence and into emerging adulthood (ages 18–24), there is a limited understanding in the literature on how these identity explorations and formations prospectively develop in early and mid-adolescence (ages 10–17), often relying on retrospective accounts (e.g., Charmaraman, Grossman, & Richer, Reference Charmaraman, Grossman and Richer2021). Many of the studies in this chapter illustrate the experiences of older youth to shed some light on how tweens or teens may have similar experiences. The less common studies that focused on younger teens and tween experiences are highlighted whenever available.

Role of Digital Media in Development of Marginalized Racial-Ethnic Identities

One form of identity that becomes an integral part of the adolescent developmental period is racial and ethnic identity formation. This particular identity formation is stratified into periods of exploration and commitment (Phinney & Ong, Reference Phinney and Ong2007; Umaña-Taylor et al., Reference Umaña-Taylor, Quintana and Lee2014), all of which are considered a point of cultural strength contributing to minority youth resiliency (Masten & Reed, Reference Masten, Reed, Snyder and Lopez2002). Early (ages 10–13) and mid-adolescence (ages 14–17) is a key period for the exploration of racial-ethnic identity prior to commitment, which occurs during development in conjunction with heightened priorities of social impact, connectedness, and autonomy (Williams et al., Reference Williams, Bolland, Hooper, Church, Tomek and Bolland2014). Theoretical research has positioned racial and ethnic identity as an internalized feeling of belonging to a particular racial-ethnic group and is thought to be formed in later adolescence and young adulthood (Phinney, Reference Phinney1990; Yip et al., Reference Yip, Seaton and Sellers2006). Compared to children’s conceptions, adolescents’ notions of race and ethnicity are more abstract and complex, which is marked by a heightened group consciousness perspective (Quintana, Reference Quintana1994). It is worth noting for this chapter that in the context of the United States, racial and ethnic minorities are individuals who identify as non-White.1 There are unique histories (e.g., slavery, internment and incarceration, segregation) tied to the individual subgroups of racial and ethnic minorities in the USA, which contribute to the upbringing and identity formation of young people today. With these histories being told and readily accessible in the era of the Internet, exposure from an earlier age of these perceptions is bound to influence the development and well-being of children and teens.

While adolescence is a salient time for exploring racial-ethnic identity, it is a complex process that involves the influence of nested ecologies surrounding an adolescent, such as family (more proximal), school, community, and political climate (more distal) (Charmaraman & Grossman, Reference Charmaraman and Grossman2010; Spencer et al., Reference Spencer, Dupree and Hartmann1997), all of which also influence youth outcomes. One might consider the ubiquitous use of technology, especially social media, among young people as an additional digital ecology that has become a larger part of the processes in racial-ethnic identity exploration. The dominance of digital media exposure and social media use in adolescence across all races and ethnicities (Anderson & Jiang, Reference Anderson and Jiang2018) has potential consequences, both negative and positive, for youth exploration of what it means to be a person of color in their communities and its effects on mental health.

Risk for Racial-Ethnic Minority Youth

Racial-ethnic differentiation inherently creates opportunities for discrimination and negative stereotypes of minoritized groups to become perpetuated through digital media, which is a well-documented stressor and risk factor for poorer outcomes (Berry, Reference Berry2000; Trent et al., Reference Trent, Dooley and Dougé2019). As offline risk factors are shown to be mirrored online (Przybylski & Bowes, Reference Przybylski and Bowes2017), discrimination on digital media, and especially social media, has increased stress during an already dynamic time of development. Racial-ethnic discrimination online comes in many forms and may include racial slurs or jokes, negative stereotyping such as “criminals” or “thugs,” body shaming of skin tone or body figure, and even threat of harm, simply due to racial-ethnic profiles. Tynes and colleagues (Reference Tynes, English, Del Toro, Smith, Lozada and Williams2020) conducted the first study of its kind to investigate the mental health implications of online discrimination among Black and Latinx adolescents (6th–12th grade) over time. This novel study reveals that increases in experienced individual and vicarious online racial discrimination among Black and Latinx adolescents increases risks for higher levels of depressive and anxiety symptoms. Uniquely, older Black adolescent males were more likely to report high exposure to online discrimination at a younger age with decreasing discrimination over time compared to Latinx adolescent males. Yet, those who experienced high and stable vicarious online discrimination and those who were exposed to high levels of individual racial discrimination online at an early age experienced worse psychological outcomes over time, regardless of gender. This example shows the distinct experience of racial-ethnic online discrimination risks of Black and Latinx adolescents. In our work at the Youth, Media, & Wellbeing Research Lab, we demonstrated that Black and Latinx adolescents (5th–9th grade) adopt social media younger than their White peers, further exposing them to behavioral health difficulties such as sleep disruption due to screen content they were exposed to (Zhai et al., Reference Zhai, Jordan, Reeves-Miller, Xiao and Charmaraman2020).

Much like the historical contexts of racial-ethnic discrimination against Black and Latinx populations in the USA, individuals of Asian heritage have been subjected to severe historical discrimination (Gee et al., Reference Gee, Ro, Shariff-Marco and Chae2009). Despite having the highest reported accessibility to the Internet and social media (Spooner, Reference Spooner2001), Asian American youth still remain underrepresented in the literature around digital media and well-being. Asian Americans are often subject to stereotypes such as the “model minorities,” “honorary Whites,” or even the perpetual foreigners (Kiang et al., Reference Kiang, Witkow and Champagne2013, p. 1714), which may have damaging effects on the racial-ethnic exploration among youth. For instance, Asian Americans in later adolescence (18–24 years) are more likely to be cyberbullied compared to White or Hispanic counterparts (Charmaraman et al., Reference Charmaraman, Chan, Chen, Richer and Ramanudom2018). At the same time, Asian Americans are the least likely to report negative occurrences on social media in order to reduce “losing face” and maintain a positive image to the external world. Studies have demonstrated that Asian Americans experience stigma and shame when it comes to their mental health problems and treatment (Surgeon General, 2001; Wang et al., Reference Wang, Barlis and Do2020), with cultural stereotypes implying that seeking professional help is a sign of weakness, lack of self-discipline, or may cause shame to the family name (Uba, Reference Uba1994). Thus, it is unsurprising that Asian American youth would withhold their emotional turmoil from the public eye on social media platforms.

A more recent example of Asian Americans feeling targeted is through the current implications of the global pandemic, which has caused a rapid resurgence of hate and racial profiling among the Asian American communities (Croucher et al., Reference Croucher, Nguyen and Rahmani2020). According to the Integrated Threat Theory (Stephan & Stephan, Reference Stephan, Stephan and Oskamp2000), this poses a realistic threat and generalized out-group stereotypes of this given event has driven the increases in discriminatory behaviors against Asian Americans, specifically Chinese Americans. Asian American adolescents are among those with highest access to the Internet and social media that leads to early exposures to these racial-ethnic discriminations online. There is emerging evidence indicating that a strong racial-ethnic and/or immigrant identity can protect against the negative effects of online harassment and depression in early adolescence (e.g., Hernandez & Charmaraman, Reference Hernandez and Charmaraman2021).

Indigenous and Native American adolescents are heavy consumers of digital media (Rushing & Stephens, Reference Rushing and Stephens2011) but are also a population vastly affected by mental health problems such as substance abuse and suicide (Park-Lee et al., Reference Park-Lee, Lipari and Bose2018). Racial-ethnic identity exploration among current Indigenous youth is often met with an internal conflict of relating immediate relevant experiences with historical cultures and traumas (Wexler, Reference Wexler2009, p. 272) that contributes to outcomes of well-being. Among Indigenous adolescents, it has been shown that perceived discrimination and historical oppression of Native American populations have been strong indicators of poor mental health outcomes such as alcohol abuse and depression (Cheadle & Whitbeck, Reference Cheadle and Whitbeck2011). Taking into consideration that offline discrimination is likely to be transferred online (Przybylski & Bowes, Reference Przybylski and Bowes2017), it can be hypothesized that exposure to racial-ethnic discrimination on digital platforms such as social media may also amplify the risk of poor mental health outcomes among Indigenous adolescents. Yet research remains extremely limited in the digital media domain for the population and should be further explored.

Resilience for Racial-Ethnic Minority Youth

As the counternarrative to risks, there is a growing body of literature focused on protective mechanisms of social technologies for youth of color. Among a cohort of racial-ethnic minority adolescents (i.e., Black, Latinx, Asian, and multiracial), research has shown a stronger sense of racial and ethnic identity centrality among Black and Latinx adolescent females showing greater identity centrality compared to males (Charmaraman & Grossman, Reference Charmaraman and Grossman2010). This is consistent with the theoretical groundwork of the phenomenological and ecological framework (PVEST) that has been applied to race and ethnic identity formation (Spencer et al., Reference Spencer, Dupree and Hartmann1997). A scoping review by Williams and Moody (Reference Williams and Moody2019) uses the PVEST framework to understand the role of identifying as a Black and female youth and its impacts on well-being in the digital age. Young Black girls are among the highest consumers of social media, and their identities are being supported in ways that are mirrored among other non-Black youth, such as elevating self-esteem and peer affirmations. But because of a long-standing history of stereotypic media portrayal of the young Black female (e.g., nurturing, aggressive, hypersexualized), these messages and stereotypes have translated onto social media that makes identity exploration increasingly complex. This exemplifies that exposure to an online space helps to amplify marginalized youths’ voices, but also amplifies the systemic issues surrounding the Black community today that plays a significant role in racial identity exploration.

A developmental consideration during adolescence is the prioritization of social connectedness, and this connectedness through shared heritage, culture, and histories can be strengthened by digital connection. Despite the systemic risk factors related to race and ethnicity that exist in the USA, there is a shift in focus away from deficit-based approaches and toward recognizing the assets and strength within these communities, especially among young people, which help them thrive in a difficult system. In terms of combating the isolation that many adolescents feel, our Youth, Media, & Wellbeing Research Lab demonstrated that Black and Latinx youth aged 11–15 were more likely than White and Asian adolescents to join online groups that made them feel less lonely and isolated (Zhai et al., Reference Zhai, Jordan, Reeves-Miller, Xiao and Charmaraman2020). These online communities included group chats on Snapchat, House Party, WhatsApp, Discord, anime fandom, and sports or hobby-related groups. In addition, Black youth preferred YouTube video content that was about relationships or friendships, whereas Latinx youth were more likely to seek opportunities to learn how to cope with stress and anxiety and to use social media to stay in touch with family and relatives compared to White youth.

Another powerful example of racial-ethnic based online communities is the Black Twitter culture that erupted in 2015. This online culture was a profound way that millions of Black community members came together to share experiences, but more importantly to create a form of resistance to the marginalization that has long-standing impacts to justice and well-being in the Black community (Florini, Reference Florini2014). A more recent study highlighted that Black adolescents are among the vast users of these online spaces to increase their social capital, but also to facilitate connections to such identity-based communities while amplifying their voices and representation online (Borough et al., Reference Borough, Literat and Ikin2020).

Latinx adolescents often feel the need to suppress the expression of their culture on social media due to potential discrimination or not enough affirmation (e.g., “likes”) compared to when they post more “Americanized” cultures like Thanksgiving or Christmas holiday posts (Borough et al., Reference Borough, Literat and Ikin2020). Despite this finding, Latinx adolescents still sought out positive aspects of expanding social capital on social media platforms that supported the prospects of job and education opportunities, which is an important factor tied to identity and well-being outcomes for this marginalized group. Another example of the strength in racial-ethnic identity in the digital age for Latinx youth is ethnic identity exploration, such that expressing higher levels of connectedness to the culture via the exploration of their identity is a protective factor against problematic externalizing and internalizing behaviors related to online racial-ethnic discrimination (Umaña-Taylor et al., Reference Umaña-Taylor, Tynes, Toomey, Williams and Mitchell2015).

There are still limited accounts of research that emphasize the opportunities and experiences of Asian American and Indigenous adolescents’ racial-ethnic identity exploration, especially during early (ages 10–13) and mid-adolescence (ages 11–17), and the role that social media and other digital ecosystems play in this process. In a mixed-method study among older adolescents (ages 18–25), Asian Americans reported using social media as a way of seeking out social support during difficult times in more privatized online channels, which is thought to be a way of navigating the stigma around mental health and impression management that reigns as a priority in many Asian cultures (Charmaraman et al., Reference Charmaraman, Chan, Chen, Richer and Ramanudom2018). Recent findings in response to the rise in racism among Asian Americans have shown online spaces to be a space of demonstrating comradery and resistance to such discrimination, similarly seen in Black Twitter, thus preventing harmful outcomes (Abidin & Zeng, Reference Abidin and Zeng2020). While this work has yet to be shown in the adolescent developmental period, this is another exemplar of the power of collective racial-ethnic identity in an online community.

Among Indigenous youth and resiliency online, while empirical work is vastly minimal, the work of an online space WeRNative to support Native teens exemplifies the unique affordances digital media can have to support the identities and well-being with a greater reach than before (Rushing et al., Reference Rushing, Stephens and Dog2018). To support Indigenous youth during a conflict in ethnic identity, there is an opportunity for digital technologies to bridge the gaps between historical contexts and current experiences to enhance the connection to the heritage of Indigenous communities. Work with Indigenous youth in content creation to address health literacy via digital media (e.g., videos) shows that this not only promotes healthy behaviors, but is also a mechanism to address stressors related to culture and ethnic identity (Stewart et al., Reference Stewart, Riecken, Scott, Tanaka and Riecken2008). Indigenous teens and emerging adults have taken to social media as a means for creative expression of the Native racial-ethnic identity and solidarity, which is said to be a way of reconnecting with the heritage and reaching a broader population of youth in this community (Monkman, Reference Monkman2020; Noor, Reference Noor2020). Such strength in racial-ethnic identification among a high-risk group of youth is imperative for support of well-being, and the expanded reach and social capital that social media provides can be vastly beneficial for developing Indigenous adolescents.

It is evident that there are risks associated with online discrimination exposure for youth of color, yet there are vast opportunities through social capital, connectedness, and empowerment that youth of color experience with social technologies. Mirroring of risks in online and offline environments can be taken into consideration when building a digital ecosystem that supports diverse groups of adolescents during this time of identity development.

Role of Digital Media in Development of Sexual and Gender Minority Identities

For a subset of youth, referred to in this chapter as LGBT+,2 processes of identity formation and development during adolescence center around sexual orientation and gender. In this context, identity development is understood as the process by which an individual attaches labels and meaning to their experiences of sexual attraction and gendered existence (Gordon & Silva, Reference Gordon and Silva2014; Robertson, Reference Robertson2013). Among social scientists, sexuality and gender are understood as social constructions; much like race, rather than manifesting in the individual as innate biological traits, they are influenced by the social forces that define normative and nonnormative behaviors (Gordon & Silva, Reference Gordon and Silva2014; Robertson, Reference Robertson2013). For instance, an adolescent attempting to articulate a minority sexual orientation might be deterred from doing so by compulsory heterosexuality, the set of societal norms that presume and dictate heterosexual behavior and identity (Robertson, Reference Robertson2013). Sexual identity development is a highly variable process, but integration of a sexual identity with other aspects of the self is often signified when individuals become comfortable with others knowing their sexuality, actively disclose their identity to others, or engage with the broader LGBT+ community (Rosario et al., Reference Rosario, Schrimshaw and Hunter2008). Gender identity is often developed through intrapersonal processes, and alongside other aspects of gender-related experience, including gender presentation and self-image. While gender norms are often even more rigid than those pertaining to sexuality, the ability to express one’s gender identity both internally and to an external social world has positive associations with well-being (Kuper et al., Reference Kuper, Wright and Mustanski2018). Although young adults often face significant interpersonal consequences when they express marginalized sexual and gender identities, group identification can also be a source of protection and well-being for LGBT+ youth (Scroggs & Vennum, Reference Scroggs and Vennum2020).

Since the Internet’s early days, digital media has provided LGBT+ users with spaces to gather, construct identity, and share content with one another. In many respects, various niche online communities today constitute “queer cultural archipelagos” (Ghaziani, Reference Ghaziani2014, p. 137): concentrated areas that, some argue, have replaced gay bars and “gayborhoods” as safe spaces for those who identify as LGBT+ (Cavalcante, Reference Cavalcante2019). As this migration online occurs, LGBT+ adolescents are being exposed to these digital spaces – and simultaneously helping to construct the cultures that define them. One study found that LGBT+ adolescents as young as 13 years old, on average, spend more time online than their heterosexual, cisgender counterparts (Palmer et al., Reference Palmer, Kosciw, Greytak, Ybarra, Korchmaros and Mitchell2013). Another study, although it did not find differences in time spent online and excluded transgender youth from its sample, was able to show that sexual minority youth aged 18–24 tended to use the Internet differently than heterosexual youth, expanding their activity across a greater variety of social networking sites and engaging more purposefully in identity development online (Ceglarek & Ward, Reference Ceglarek and Ward2016). While existing scholarship has begun to examine the ways in which LGBT+ young adults navigate cyberspace, LGBT+ youth, especially those under 18 years old, are still a critically understudied population. Research on the ways transgender youth navigate social media remains especially rare.

Often, the ways in which LGBT+ youth learn about themselves and their communities online are directly related to identity development. This type of online engagement may take many forms, including traditional learning, in which users seek out information about identity-related terminology and then apply these to their own experiences; social learning, in which users observe and identify LGBT+ role models on social media; experiential learning, which involves active participation in the online LGBT+ community, especially through the use of dating apps; and teaching others, which occurs when LGBT+ individuals use social media platforms to provide others with information on LGBT+ issues, including experiences with coming out (Fox & Ralston, Reference Fox and Ralston2016). While each of these processes allow LGBT+ youth to better define their personal, social, and collective identities, this digital learning also incorporates an understanding of the stressors that LGBT+ youth may face when they actively express and practice their gender and sexuality online.

A commonly used term used to discuss social networking’s impact on LGBT+ youth well-being is context collapse: a phenomenon that occurs when the individual, by sharing content on a social media platform, exposes that content to a variety of different audiences, some of whom may not respond positively (Fox & Ralston, Reference Fox and Ralston2016; McConnell et al., Reference McConnell, Néray, Hogan, Korpak, Clifford and Birkett2018). For LGBT+ youth, this conflict is particularly salient, since people they know in various social contexts may have drastically different levels of awareness about their sexuality or gender identity. Context collapse can therefore profoundly impact the ways in which young LGBT+ people navigate disclosure and the coming out process. Coming out itself has complex associations with well-being; while it can positively influence the lives of LGBT+ youth in certain relational contexts, in other contexts it can limit identity formation or negatively impact mental health (McConnell et al., Reference McConnell, Néray, Hogan, Korpak, Clifford and Birkett2018, p. 3).

Many LGBT+ individuals seem able to circumvent some of the difficulties associated with context collapse by dividing their online activity between a variety of social media sites. DeVito et al. (Reference DeVito, Walker and Birnholtz2018) argue that for LGBT+ users, social media activity should be conceptualized as an ecosystem, that is, users are able to manage their self-presentation by targeting content to different audiences on different platforms, in addition to the use of privacy controls within one platform. Examining interactions on specific platforms allows researchers to define some of the key characteristics of the LGBT+ adolescents’ online ecosystems. For instance, on Facebook, a platform where users primarily interact with people they already have relationships with offline, LGBT+ youth seem to subscribe to the lowest common denominator model, in which they tailor identity presentations toward whichever audiences are most likely to express disapproval toward them (McConnell et al., Reference McConnell, Néray, Hogan, Korpak, Clifford and Birkett2018). Tumblr, meanwhile, has had success engaging young LGBT+ users, which is often attributed to its features that enable LGBT+ youth to connect to others in the LGBT+ community with minimal threat of exposing their identities, such as anonymity and the privileging of content sharing over content creation (Cavalcante, Reference Cavalcante2019).

As LGBT+ youth come of age on the Internet, social media provides a space for them to cultivate personal, social, and collective identities. In some cases, this process occurs as learning, primarily positive interactions that allow individuals to practice being LGBT+ in relative safety and connection with others. However, LGBT+ participation online coexists with the awareness that nonnormative experiences of sexuality and gender may incur negative social responses. This danger forces LGBT+ youth to navigate coming out and expressing identity with care, manifesting in differential usage of social media platforms, which itself can affect adolescent well-being.

Risk for Sexual/Gender Minority Youth

Existing scholarship on LGBT+ populations’ activity online has identified the Internet as a space where youth can be exposed to harassment, discrimination, and other forms of bullying that may be easier to perpetrate in online spaces. Multiple studies have found that LGBT+ youth are more likely to be harassed online than non-LGBT+ youth (Palmer et al., Reference Palmer, Kosciw, Greytak, Ybarra, Korchmaros and Mitchell2013; Ybarra et al., Reference Ybarra, Mitchell, Palmer and Reisner2015). Cyberbullying is perpetrated against LGBT+ youth in a variety of ways, including verbal victimization, relational victimization, and electronic actions, all of which are often combined with in-person harassment (Varjas et al., 2013). These distinctions highlight the variety of modes through which the cyberbullying of LGBT+ youth can occur, including sexual harassment, the use of slurs, purposeful social exclusion, and the targeting of social media content using viruses. It is also notable that, in a sample that did not include gender minority youth, several instances were identified in which the LGB adolescents interviewed were themselves perpetrators of bullying, including online verbal harassment (Varjas et al., 2013); this finding complicates the assumption that sexual minority youth are solely victims in their online interactions.

The effects of online harassment include increased depression and suicidality among LGBT+ youth (Schimmel-Bristow & Ahrens, Reference Schimmel-Bristow, Ahrens, Moreno and Radovic2018), dangers that are especially salient given that LGBT+ youth are particularly vulnerable to cybervictimization, since revealing their experiences to parents may mean that they risk coming out or losing access to digital technologies (Cooper & Blumenfeld, Reference Cooper and Blumenfeld2012). However, it is possible that the role of cyberbullying in LGBT+ adolescents’ digital landscape may be shifting. Data collected in the fall of 2019 by our Youth, Media, and Wellbeing Lab, for instance, found no difference between the amount of heterosexual and sexual minority youth who reported experiencing cyberbullying online. Our sample included children under 13, of which 25% experienced nonheterosexual attraction (Charmaraman, Hodes, & Richer, Reference Charmaraman, Grossman and Richer2021). However, there are several indicators that sexual minority youth today may experience more social isolation online than their peers do. These youth tended to have fewer friends on social media, and were less likely to use social media to engage positively with friends, including sharing content that was comedic or that they enjoyed. They also were less likely to be friends with family members, peers, or acquaintances on their social media networks, indicating that the links between in-person and online communities may be weaker for LGBT+ youth than other adolescents. Sexual minority youth also reported feeling isolated more often than heterosexual youth. Therefore, there is reason to be concerned that even when young LGBT+ populations are not directly attacked online, they still experience victimization via structural exclusion from the heteronormative social circles that make up their real-world contacts.

As a consequence of context collapse, LGBT+ youth also often find themselves at heightened risk when they share personal information online. Our Youth, Media, and Wellbeing Lab found that sexual minority youth were less likely to have private settings on their social media accounts (Charmaraman, Hodes, & Richer, Reference Charmaraman, Grossman and Richer2021), and Varjas et al. (2013) discussed sexual minority teenagers’ willingness to share personal information with those they talked to virtually as a possible drawback of online activity. Panizo (Reference Panizo2018), in a study of teenagers aged 14–19 in Spain who identified as gay, also noted the recurrence of anecdotes in which teenagers’ disclosure of their sexual orientation online was discovered by relatives, forcing them “out of the closet indirectly and involuntarily” (p. 67). While these results are open to further interpretation, they do imply that LGBT+ youth place themselves at higher risk when sharing information about themselves through digital media due to the stark division that sometimes exists between their expression of identity online and offline.

Finally, Youth, Media, and Wellbeing Lab data shows that sexual minority youth report seeing more content related to self-harm on social media and are more likely to have actually attempted self-harm (Charmaraman, Hodes, & Richer, Reference Charmaraman, Grossman and Richer2021). These sexual minority youth were also found to have higher depressive scores. These findings are in line with concerns about the potential of specific sites, like Tumblr, to foster dangerous subcultures that correspond with social isolation and poor mental health outcomes (Cavalcante, Reference Cavalcante2019).

Resilience for Sexual/Gender Minority Youth

Despite its documented risks, digital media use often provides numerous ways for LGBT+ youth to build resilience. Many forms of online resilience-building are closely related to the process of identity formation. Hillier and Harrison (Reference Hillier and Harrison2007) were among the first to argue that internet communities constitute safe spaces for LGBT+ youth who face hostile environments at home or school. In their study of same-sex attracted Australian youth aged 14–21, they assert that in digital spaces, anonymity and the lack of geographic boundaries provide the ideal practice ground for constructing coming out narratives, engaging with a communal gay culture, experimenting with nonheterosexual intimacy, and socializing with other same-sex-attracted youth. Sexual minority youth have been found to perceive their online friends as significantly more socially supportive than their in-person friends, and LGBT+ youth are more likely to have friends they only know online. Despite the finding that youth across sexual and gender identities feel relatively safe online, researchers note that strong online social support still does not appear to reduce the likelihood of online or in-person harassment and victimization (Ybarra et al., Reference Ybarra, Mitchell, Palmer and Reisner2015). The Youth, Media, and Wellbeing Lab also found that sexual minority youth they surveyed were more likely to join an online group in order to reduce social isolation or feelings of loneliness (Charmaraman, Hodes, & Richer, Reference Charmaraman, Grossman and Richer2021), which similarly implies that LGBT+ youth have been able to engage with social media networks in supportive and fortifying ways.

Hillier and Harrison (Reference Hillier and Harrison2007) also note that accessing resources pertaining to sexual orientation, sexual health, and sexual identity can be a critical form of internet use for same-sex-attracted youth, a utility that is echoed in other studies of LGBT+ adolescents. Fox and Ralston (Reference Fox and Ralston2016) reported that participants used online resources to educate themselves about terminology related to sexual orientation and gender identity, to learn about gender transition, and, in a crossover with their offline context, to identify LGBT+ spaces in physical proximity to them. The Internet can also be a useful tool to identify LGBT+-friendly physicians, therapists, and other care providers (Schimmel-Bristow & Ahrens, Reference Schimmel-Bristow, Ahrens, Moreno and Radovic2018).

A final form of resilience-building, also with its roots in identity development, is the use of online platforms as springboards for LGBT+ activism. Education nonprofit GLSEN reported that LGBT+ youth aged 13–18 were about twice as likely as non-LGBT+ youth to participate in civic engagement activities, and 77% had been part of an online community in support of a social cause (Palmer et al., Reference Palmer, Kosciw, Greytak, Ybarra, Korchmaros and Mitchell2013). Connection to online community fosters sexual citizenship, which occurs when one’s politicized identity prompts one to engage in social activism (Robards et al., Reference Robards, Churchill, Vivienne, Hanckel, Byron, Aggleton, Cover, Leahy, Marshall and Rasmussen2019). Thus, social media often serves as a tool for LGBT+ youth to communicate about social issues that impact them, and allows them to build strengthened connections to both their immediate and virtual communities.

Ultimately, it is clear that despite the potential of facing victimization, LGBT+ youth wield considerable agency in their online interactions. Much of the time, their vulnerabilities coexist with a demonstrated ability to navigate digital space, in ways that positively supplement or contrast with their offline environments.

Role of Digital Media in the Development of Other Marginalized Youth Identities

In this section, we explore how digital media influences the identities, risk, and resilience of youth from other marginalized backgrounds, ranging from those living in disadvantaged neighborhoods to homeless and neurodiverse youth.

In the case of youth in disadvantaged neighborhoods, Oldenburg (Reference Oldenburg1989) argues that high levels of poverty, decreased employment prospects, and the lack of safe gathering spaces without threat of violence or drug activity lead to a problem of place. These urban youth often have a dilemma of geographic identity – at once proud and connected to one’s neighborhood but needing a third space to feel safe and secure to hang out. Soukup (Reference Soukup2006) articulated a “digital third space” wherein online communities are key to developing one’s neighborhood identity and can be located within a local geographic area, allowing participants to be fully immersed in a computer-mediated environment contributing to a sense of connectedness and sense of refuge.

Homelessness is often an invisible identity that is intentionally hidden from outsiders such as classmates at school or future employers (Whitbeck & Hoyt, Reference Whitbeck and Hoyt1999). The majority of research on the digital media use of homeless youth focuses on health information seeking (Eyrich-Garg, Reference Eyrich-Garg2010) and less on social connections with others. Prior research on nonhomeless youth suggests that having a cell phone in one’s possession increases feelings of safety and security while on the move, and merely owning a cell phone makes youth feel socially connected (Wei & Lo, Reference Wei and Lo2006). This may be a particularly salient part of homeless youths’ identities – having a lifeline to a networked world may be more critical to maintain those connections they most value.

Prior research has demonstrated that social media has provided people with intellectual disabilities an opportunity to express their preferred personal and social identities (Caton & Chapman, Reference Caton and Chapman2016), which may include reflections on their identities as neurodiverse, but also serves as an online space where they can be just like everyone else. For instance, in a study with people with Down syndrome, online profiles were places to be vocal about their thoughts, feelings, and needs (Seale, Reference Seale2007). Studies have shown those with intellectual disabilities publicize their disability in blogs, even when these online venues provided space to focus on other aspects of their lives (McClimens & Gordon, Reference McClimens and Gordon2008). Other research has observed that some individuals with intellectual disabilities prefer to not mention the label of intellectual disability in an online profile, providing a chance to escape the identity stigma associated with these disabilities (Löfgren-Mårtenson, Reference Löfgren-Mårtenson2008).

Risk of Other Marginalized Youth

Adolescents from lower-income households have been found to spend on average an hour and a half more on screens than their higher-income peers (George et al., Reference George, Jensen and Russell2020). They are also more likely to be passively viewing content and less frequently using screens for research and learning (OECD, 2016). In the new digital divide of remote learning (Odgers & Robb, Reference Odgers and Robb2020), lower-income households not only have less digital access but also fewer adults who can scaffold digital support, which is critical given the increased risk for mental health symptoms.

In a study by VonHoltz and colleagues (Reference VonHoltz, Frasso, Golinkoff, Lozano, Hanlon and Dowshen2018), individuals who do not have easy access to the Internet, such as may be the case with youth experiencing homelessness, demonstrate the need to be more purposeful when using public computers. For instance, using the Internet for social media is limited when other basic needs are not being met, such as housing, food, and unemployment. When youth do not have easy access to health care or resources to understand their health ailments, they turn to the Internet to self-diagnose, often finding the terminology and sheer volume of information to be too complex. In terms of being connected with others online, young homeless women have been found to be less likely to stay in touch with friends and less likely to post public messages, signaling a weaker social network to rely on and a greater likelihood of social isolation (Guadagno et al., Reference Guadagno, Muscanell and Pollio2013).

Prior research on social technology use among adolescents with physical or intellectual health conditions, such as autism spectrum disorder, have focused on their unique challenges in understanding social situations and managing peer relationships. This can lead individuals with disabilities to turn to technology as a less threatening way of interacting with others (Davidson, Reference Davidson2008). Unfortunately, having a noticeable or visible disability increases the chances of being a victim of cyberbullying, particularly for those who use the Internet more frequently and are already bullied in person (Kowalski et al., Reference Kowalski, Morgan, Drake-Lavelle and Allison2016). People with intellectual disabilities have also been found to disclose more personal information about themselves and photos online, increasing the potential for financial, sexual, and personal safety threats (Holmes & O’Loughlin, Reference Holmes and O’Loughlin2014). Adolescents with a diagnosis of attention deficit hyperactivity disorder (ADHD) have been shown to not only be likely victims but also perpetrators of cyberbullying peers. Those with ADHD who were victimized reported higher incidents of loneliness and lower levels of self-efficacy and social support compared to nonvictims (Heiman et al., Reference Heiman, Olenik-Shemesh and Eden2014).

Resilience of Other Marginalized Youth

Digital media and mobile technology access may be especially difficult for homeless youth who are also at increased risk for behavioral and mental health problems associated with substance abuse and violence, compared to housed youth (Rice et al., Reference Rice, Milburn, Rotheram-Borus, Mallett and Rosenthal2005). Despite the barriers, studies have dispelled the myth of a digital divide for homeless youth, such that around 85% of the homeless population access the Internet at least once a week and 62% of homeless youth had a cell phone, mostly related to instrumental purposes, such as looking for jobs or staying connected with social workers trying to track them down (Rice et al., Reference Rice, Lee and Taitt2011). Besides using their personal devices, homeless youth are accessing the Internet through social service agencies (60%), public libraries (54%), and internet cafes (14%) (Pollio et al., Reference Pollio, Batey, Bender, Ferguson and Thompson2013). Only 9% of homeless youth indicated that they did not have a social media profile (Young & Rice, Reference Young and Rice2011). Rice and colleagues (Reference Rice, Lee and Taitt2011) found that homeless youth are most likely to stay in touch via cell phones with friends they knew before they were homeless, followed by siblings, parents, and street-based peers, which underlines the critical social network that friends can provide for these youth. Besides studies on digital access and seeking health-related information, there is limited research examining what homeless youth actually communicate about on their social media sites. These studies have found that youth discussed both risk-taking behaviors such as having sex with someone they met online or drug use, but also prosocial discussion topics such as school, family, work, setting goals, and even their homelessness (Barman-Adhikari et al., Reference Barman-Adhikari, Rice, Bender, Lengnick-Hall, Yoshioka-Maxwell and Rhoades2016).

Studies focused on youth with intellectual or socioemotional disabilities are almost always centered on cyberbullying and the promise of technology-facilitated interventions (Schimmel-Bristow & Ahrens, Reference Schimmel-Bristow, Ahrens, Moreno and Radovic2018), rather than how these young people use social media in resilient ways. A recent review suggested that potential benefits of social media use in young people with intellectual disabilities include increased opportunities to make and maintain relationships, decreased loneliness (Kydland et al., Reference Kydland, Molka-Danielsen, Balandin and Fallmyr2012), increasing self-confidence and self-esteem through learning new technical skills, and having fun (Caton & Chapman, Reference Caton and Chapman2016).

Challenges and Future Directions
Moving Beyond Differential Access

Researchers have recently made a call to action on moving away from quantity of time spent on digital technologies, and more toward understanding the quality of experiences online that may have larger impacts to youth well-being (Ito et al., Reference Ito, Odgers and Schueller2020; Odgers & Jensen, Reference Odgers and Jensen2020). In doing so, the research will be able to provide evidence for how the most pervasive forms of digital media in the current moment is impacting the lives of adolescents, especially those who are marginalized and understudied. In the case of youth who are homeless, access to digital technologies (e.g., mobile phones and public computers) and being able to keep in touch with loved ones is a primary concern for both the research participants and the researchers who study them. However, little is known about which social media platforms are being accessed by this vulnerable population and for what purposes, how often, etc.

Hard to Reach and Hidden Subpopulations

Racial-ethnic identity formation during adolescence is met with many challenges and opportunities in the digital age, especially among the growing diversity in the population. While we have only scratched the surface of the possible implications that this identity development process can have in online and social media spaces, there is still much to be explored. A major challenge that research has going forward is accounting for the wide range of races and ethnicities within the USA, and accounting for bi- and multiracial-ethnic identities. There are also many approaches to mapping out the racial-ethnic identity development during this critical period of adolescence, and prior research has had a stronger focus on the identity commitment during late adolescence and young adulthood (e.g., college samples). As digital media and social media adopters are becoming younger at a rapid rate, we must further explore how the pervasive nature of constant exposure and use affects racial and ethnic identity development in the earlier stages of adolescence. Parents and educators might consider discussions with youth from marginalized backgrounds to prepare for biased language and to arm them with the tools to be proactive with learning about and/or establishing their social identities online.

Despite the fact that some youth may identify as LGBT+ at ages as young as 9 (Calzo & Blashill, Reference Calzo and Blashill2018), research about LGBT+ adolescent behavior online is extremely limited for populations under 18 years old. Information about the digital media use of LGBT+ children under 13 years old is virtually nonexistent, and the Youth, Media, and Wellbeing Lab’s data is among the only to date that include children in middle school. Much of the existing research also fails to include transgender youth in its samples, or frames its analyses of this population as secondary to findings about LGB individuals. Thus, future research on LGBT+ social media use has an opportunity to focus on each of these vulnerable populations. As children gain access to social media earlier in middle school, and even in late elementary school, information about how they begin to develop LGBT+ identity or learn about gender and sexuality can provide important context for parents and teachers. Transgender youth, meanwhile, face unique barriers to positive identity formation throughout their developmental years (Palmer et al., Reference Palmer, Kosciw, Greytak, Ybarra, Korchmaros and Mitchell2013); therefore, research devoted to the mental health impacts of transgender digital media use, especially as compared to other members of the LGBT+ community, is a valuable area for future exploration.

Adolescent development may also be compounded with intersectional identity formations. In a diverse mixed-method study of adolescents and young adults aged 12–25, Charmaraman and colleagues (Reference Charmaraman, Chan, Price, Richer, Tassie and Brown2015) found that girls and women of color participated in more online blogs and were more likely to report revealing their stress on social media compared to both White and male participants. The unique issues faced by LGBT+ youth who are racial minorities or have other marginalized identities are also understudied, such that race overlaps with terms used and content posted about sexuality (e.g., Wargo, Reference Wargo2016). More expansive qualitative and mixed-method research is necessary to understand how particular experiences of sexual orientation and gender are racialized differently online. GLSEN also suggests that lack of internet access for LGBT+ youth living in rural areas merits future investigation, since many of these adolescents are already isolated from any form of LGBT+ community (Palmer et al., Reference Palmer, Kosciw, Greytak, Ybarra, Korchmaros and Mitchell2013).

Social Media Site Affordances/Hindrances

As noted in earlier sections, it is shown that collective online spaces for interactive and passive use such as social media platforms (e.g., Twitter, Instagram, Facebook, TikTok) have proven to be a space of racial-ethnic empowerment for young people and a way to promote the social capital needed to support well-being during adolescent development. Despite the dark side of the online ecosystems related to racial discrimination and injustices in the algorithmic makeup of these social media spaces, there are vast opportunities for these tools to be utilized to support historically marginalized racial and ethnic youth to navigate and build their identities to promote mental well-being. For instance, Facebook conspicuously does not allow users to define their race on their profiles, but users can display their cultural background through their photos or interests. The opportunities must also be promoted by the industry by deviating away from a color-blind and utiopic cyberspace approach, which often further perpetuates the visual classification of other and hampers empowerment of cultural identities (Grasmuck et al., Reference Grasmuck, Martin and Zhao2009). More collaborative research with tech industry user experience teams will improve evidence-based decisions around marginalized youth who are primary users of these apps.

LGBT+ activity on newer social media sites, and the ongoing evolution of these communities’ online presence, also provide fertile ground for future research. For instance, the video-sharing app TikTok has experienced a surge of popularity among adolescents and corners of the app are primarily devoted to LGBT+ social support and resource sharing (Carey, Reference Carey2020; Ohlheiser, Reference Ohlheiser2020). At the same time, several sites, including Tumblr and YouTube, have received criticism for implementing guidelines that, while intended to prevent youth from seeing pornographic content, restrict access to LGBT+ media and resources (Romano, Reference Romano2019; Sybert, Reference Sybert2021); these actions could significantly impact LGBT+ engagement on these platforms. Simpson and Semaan (Reference Simpson and Semaan2020) have detailed the affirming yet fraught relationship many LGBT+ users form with TikTok specifically, and the platform’s potential for algorithmic exclusion. Finally, certain platforms provide researchers with the opportunity to gather data that is more representative of LGBT+ populations, as demonstrated by Salk et al. (Reference Salk, Thoma and Choukas-Bradley2020); their methodology, in which transgender youth were recruited via targeted social media advertising, has exciting implications for investigators committed to more effectively understanding the unique factors that impact LGBT+ young adults’ digital media use.

Across all of the marginalized populations in this chapter, there are untapped research avenues regarding identity work in online spaces. It is worth recognizing, like many other vulnerable youth communities, offline risk factors such as bullying, victimization and behavioral problems spill over into online spaces, which reinforces heightened risks for negative experiences on social media. It is critical that researchers and technology developers recognize the potential amplification of risks tied to one’s identity of being a part of this particular vulnerable adolescent population (Odgers, Reference Odgers2018). Moving beyond the deficits-based discourse, future research and practice can capitalize on assets-based and empowerment approaches to positive minority youth development in digital spaces. Being a member of a group that is overlooked or faced with discrimination can galvanize individuals with a sense of purpose, tackling a mutual goal of collective sense-making and more authentic visibility, which, in turn, can promote healthy youth development (Wexler et al., Reference Wexler2009). Partnerships with educators, families, clinicians, and the sociotechnical industry can further increase understanding about how to design inclusive online environments and circumstances that can lead to a digital ecosystem that ultimately supports identity development and emotional well-being.

Footnotes

3 Digital Media and the Dual Aspect of Adolescent Identity Development The Effects of Digital Media Use on Adolescents’ Commitments and Self-Stories

4 Peer Relationship Processes in the Context of Digital Media

5 Digital Media and the Developing Brain

6 Adolescents’ Digital Media Interactions within the Context of Sexuality Development

7 Culture and Digital Media in Adolescent Development

8 Marginalized and Understudied Populations Using Digital Media

Research reported in this publication was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health under award number 1R15HD094281–01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We wish to thank Amanda M. Richer for data management, Alyssa Gramajo for project coordination, Julie Parker for help with translation to a broader audience, and our undergraduate students (Teresa Xiao, Emily Zhai, Kensy Jordan, and Tulani Reeves-Miller) for their contributions to the Diversity Challenge and copyediting assistance.

References

References

Adler, J. A., Dunlop, W. L., Fivush, R., et al. (2017). Research methods for studying narrative identity: A primer. Social Psychological and Personality Science, 8(5), 519527. https://doi.org/10.1177/1948550617698202CrossRefGoogle Scholar
Arora, R. (2021, March 27). If Teen Vogue can fire an editor for her teenage tweets, no one is safe. New York Post. https://nypost.com/2021/03/27/if-alexi-mccammond-can-be-fired-for-teenage-tweets-nobodys-safe/Google Scholar
Azmitia, M., Syed, M., & Radmacher, K. (2013). Finding your niche: Identity and emotional support in emerging adults’ adjustment to the transition to college. Journal of Research in Adolescence, 23(4), 744761. https://doi.org/10.1111/jora.12037CrossRefGoogle Scholar
Baker, D. A., & Algorta, G. P. (2016). The relationship between online social networking and depression: A systematic review of quantitative studies. Cyberpsychology, Behavior, and Social Networking, 19(11), 638648. https://doi.org/10.1089/cyber.2016.0206CrossRefGoogle ScholarPubMed
BBC News. (2020, February 27). YouTube ‘not a public forum’ with guaranteed free speech. https://www.bbc.com/news/technology-51658341Google Scholar
Bennett, L. (2014). ‘If we stick together we can do anything’: Lady Gaga fandom, philanthropy and activism through social media. Celebrity Studies, 5(1–2), 138152. https://doi.org/10.1080/19392397.2013.813778CrossRefGoogle Scholar
Best, P., Manktelow, R., & Taylor, B. (2014). Online communication, social media and adolescent wellbeing: A systematic narrative review. Children and Youth Services Review, 41, 2736. https://doi.org/10.1016/j.childyouth.2014.03.001CrossRefGoogle Scholar
Beyers, W., & Luyckx, K. (2016). Ruminative exploration and reconsideration of commitment as risk factors for suboptimal identity development in adolescence and emerging adulthood. Journal of Adolescence, 47, 169178. https://doi.org/10.1016/j.adolescence.2015.10.018CrossRefGoogle ScholarPubMed
Bisgin, H., Agarwal, N., & Xu, X. (2012). A study of homophily on social media. World Wide Web, 15, 213232. https://doi.org/10.1007/s11280–011-0143-3CrossRefGoogle Scholar
Blagov, P. S., & Singer, J. S. (2004). Four dimensions of self-defining memories (specificity, meaning, content, and affect) and their relationships to self-restraint, distress, and repressive defensiveness. Journal of Personality, 72(3), 481511. https://doi.org/10.1111/j.0022-3506.2004.00270.xCrossRefGoogle ScholarPubMed
Bloom, B. S. (1953). Thought-processes in lectures and discussions. Journal of General Education, 7(3), 160169.Google Scholar
Bosma, H. A., & Kunnen, E. S. (2001). Determinants and mechanisms in ego identity development: A review and synthesis. Developmental Review, 21(1), 3966. https://doi.org/10.1006/drev.2000.0514CrossRefGoogle Scholar
Boursier, V., & Manna, V. (2018). Selfie expectancies among adolescents: Construction and validation of an instrument to assess expectancies toward selfies among boys and girls. Frontiers in Psychology, 9, Article 839. https://doi.org/10.3389/fpsyg.2018.00839CrossRefGoogle ScholarPubMed
boyd, d., & Heer, J. (2006, January 4–7). Profiles as conversation: Networked identity performance on Friendster [Paper presentation]. Proceedings of the 39th Annual Hawaii International Conference on System Sciences, Kunai, HI, United States. https://doi.org/10.1109/HICSS.2006.394Google Scholar
Bright, L. F., Kleiser, S. B., & Grau, S. L. (2015). Too much Facebook? An exploratory examination of social media fatigue. Computers in Human Behavior, 44, 148155. https://doi.org/10.1016/j.chb.2014.11.048CrossRefGoogle Scholar
Buell Hirsch, P. (2014). Clicks or commitment: activism in the age of social media. Journal of Business Strategy, 35(5), 5558. https://doi.org/10.1108/JBS-07-2014-0086CrossRefGoogle Scholar
Carr, C. T., Wohn, D. Y., & Hayes, R. A. (2016). as social support: Relational closeness, automaticity, and interpreting social support from paralinguistic digital affordances in social media. Computers in Human Behavior, 62, 385393. https://doi.org/10.1016/j.chb.2016.03.087CrossRefGoogle Scholar
Casserly, M. (2011, January 26). Multiple personalities and social media: The many faces of me. Forbes. https://www.forbes.com/sites/meghancasserly/2011/01/26/multiple-personalities-and-social-media-the-many-faces-of-me/Google Scholar
Choukas-Bradley, S., Nesi, J., Widman, L., & Galla, B. M. (2020). The Appearance-Related Social Media Consciousness Scale: Development and validation with adolescents. Body Image, 33, 164174. https://doi.org/10.1016/j.bodyim.2020.02.017CrossRefGoogle ScholarPubMed
Comunello, F., Mulargia, S., & Parisi, L. (2016). The ‘proper’ way to spread ideas through social media: Exploring the affordances and constraints of different social media platforms as perceived by Italian activists. The Sociological Review, 64(3), 515532. https://doi.org/10.1111/1467-954X.12378CrossRefGoogle Scholar
Davis, K., & Weinstein, E. C. (2017). Identity development in the digital age: An Eriksonian perspective. In Wright, M. F. (Ed.), Identity, sexuality, and relationships among adults in the digital age (pp. 17). IGI Global.Google Scholar
De Salve, A., Guidi, B., Ricci, L., & Mori, P. (2018). Discovering homophily in online social networks. Mobile Networks and Applications, 23(6), 17151726. https://doi.org/10.1007/s11036–018-1067-2CrossRefGoogle Scholar
Dhir, A., Kaur, P., Chen, S., & Pallesen, S. (2019). Antecedents and consequences of social media fatigue. International Journal of Information Management, 48, 193202. https://doi.org/10.1016/j.ijinfomgt.2019.05.021CrossRefGoogle Scholar
Eichhorn, K. (2019). The end of forgetting: Growing up with social media. Harvard University Press.CrossRefGoogle Scholar
Erikson, E. H. (1968). Identity: Youth and crisis. Norton.Google Scholar
Facebook Business. (2018, April 4). Restricting data access and protecting people’s information on Facebook. https://www.facebook.com/business/news/restricting-data-access-and-protecting-peoples-information-on-facebookGoogle Scholar
Fivush, R., Haden, C. A., & Reese, E. (2006). Elaborating on elaborations: Role of maternal reminiscing style in cognitive and socioemotional development. Child Development, 77(6), 15681588. https://doi.org/10.1111/j.1467-8624.2006.00960.xCrossRefGoogle ScholarPubMed
Gordon, S. (2020, July 13). 5 ways social media affects teen mental health. Verywell Family. https://www.verywellfamily.com/ways-social-media-affects-teen-mental-health-4144769Google Scholar
Granic, I., Morita, H., & Scholten, H. (2020). Beyond screen time: Identity development in the digital age. Psychological Inquiry, 31(3), 195223. https://doi.org/10.1080/1047840X.2020.1820214CrossRefGoogle Scholar
Gray, M. L. (2009). Out in the country: Youth, media, and queer visibility in rural America. New York University Press.Google Scholar
Griffioen, N., Van Rooij, M. M., Lichtwarck-Aschoff, A., & Granic, I. (2020). A stimulated recall method for the improved assessment of quantity and quality of social media use. Journal of Medical Internet Research, 22(1), Article e15529. https://doi.org/10.2196/15529CrossRefGoogle ScholarPubMed
Grotevant, H. D. (1987). Toward a process model of identity formation. Journal of Adolescent Research, 2(3), 203222. https://doi.org/10.1177/074355488723003CrossRefGoogle Scholar
Habermas, T., & Bluck, S. (2000). Getting a life: The emergence of the life story in adolescence. Psychological Bulletin, 126(5), 748769. https://doi.org/10.1037/0033-2909.126.5.748CrossRefGoogle ScholarPubMed
Habermas, T., & de Silveira, C. (2008). The development of global coherence in life narratives across adolescence: Temporal, causal, and thematic aspects. Developmental Psychology, 44(3), 707721. https://doi.org/10.1037/0012-1649.44.3.707CrossRefGoogle ScholarPubMed
Hammack, P. L. (2008). Narrative and the cultural psychology of identity. Personality and Social Psychology Review, 12(3), 222247. https://doi.org/10.1177/1088868308316892CrossRefGoogle ScholarPubMed
Hayes, R. A., Carr, C. T., & Wohn, D. Y. (2016). It’s the audience: Differences in social support across social media. Social Media + Society, 2(4), 112. https://doi.org/10.1177/2056305116678894CrossRefGoogle Scholar
Heins, M. (2014, June 20). The brave new world of social media censorship. Harvard Law Review. https://harvardlawreview.org/2014/06/the-brave-new-world-of-social-media-censorshipGoogle Scholar
Hemsley, J. (2019, July 11). Social media giants are restricting research vital to journalism. Columbia Journalism Review. https://www.cjr.org/tow_center/facebook-twitter-api-restrictions.phpGoogle Scholar
Hermans, H. J. M. (2004). Introduction: The dialogical self in a global and digital age. An International Journal of Theory and Research, 4(4), 297320. https://doi.org/10.1207/s1532706xid0404_1Google Scholar
Hy, L. X., & Loevinger, J. (1996). Measuring ego development (2nd ed.). Erlbaum.Google Scholar
Jargon, J. (2020, February 20). Teens are deleting Instagrams almost as fast as they post them. Wall Street Journal. https://www.wsj.com/articles/teens-are-deleting-instagrams-almost-as-fast-as-they-post-them-11582021801Google Scholar
Keles, B., McCrae, N., & Grealish, A. (2019). A systematic review: The influence of social media on depression, anxiety and psychological distress in adolescents. International Journal of Adolescence and Youth, 25(1), 7993. https://doi.org/10.1080/02673843.2019.1590851CrossRefGoogle Scholar
Kerpelman, J. L., Pittman, J. F., & Lamke, L. K. (1997). Toward a microprocess perspective on adolescent identity development: An identity control theory approach. Journal of Adolescent Research, 12(3), 325346. https://doi.org/10.1177/0743554897123002CrossRefGoogle Scholar
Kroger, J. (2004). Identity in adolescence: The balance between self and other (3rd ed.). Routledge.Google Scholar
Kroger, J., & Green, K. E. (1996). Events associated with identity status change. Journal of Adolescence, 19(5), 477490. https://doi.org/10.1006/jado.1996.0045CrossRefGoogle ScholarPubMed
Kuiper, N., Kirsh, G., & Maiolino, N. (2016). Identity and intimacy development, humor styles, and psychological well-being. Identity, 16(2), 115125. https://doi.org/10.1080/15283488.2016.1159964CrossRefGoogle Scholar
Lewis, B. (2021, January 17). The Trump ban across social media wasn’t censorship – it was a series of editorial decisions by media companies that call themselves social platforms. Business Insider. https://www.businessinsider.com/trump-ban-wasnt-censorship-it-was-an-editorial-decision-2021-1?international=true&r=US&IR=TGoogle Scholar
Loevinger, J. (1976). Ego development: Conceptions and theories. Jossey-Bass.Google Scholar
Loevinger, J. (1987). Paradigms of personality. Freeman.Google Scholar
Lomborg, S., & Bechmann, A. (2014). Using APIs for data collection on social media. The Information Society, 30(4), 256265. https://doi.org/10.1080/01972243.2014.915276CrossRefGoogle Scholar
Luyckx, K., Goossens, L., & Soenens, B. (2006). A developmental contextual perspective on identity construction in emerging adulthood: Change dynamics in commitment formation and commitment evaluation. Developmental Psychology, 42(2), 366380. https://doi.org/10.1037/0012-1649.42.2.366CrossRefGoogle ScholarPubMed
Luyckx, K., Klimstra, T. A., Duriez, B., Van Petegem, S., Beyers, W. (2013). Personal identity processes from adolescence through the late 20s: Age trends, functionality, and depressive symptoms. Social Development, 22(4), 701721. https://doi.org/10.1111/sode.12027CrossRefGoogle Scholar
Marcia, J. E. (1966). Development and validation of ego-identity status. Journal of Personality and Social Psychology, 3(5), 551558. https://doi.org/10.1037/h0023281CrossRefGoogle ScholarPubMed
Marcia, J. E. (1989). Identity and intervention. Journal of Adolescence, 12(4), 401410. https://doi.org/10.1016/0140-1971(89)90063-8CrossRefGoogle ScholarPubMed
Maslow, A. H. (1970). Motivation and personality (2nd ed.). Harper & Row.Google Scholar
McAdams, D. P. (1998). Ego, trait, identity. In Westenberg, P. M., Blasi, A., & Cohn, L. D. (Eds.), Personality development: Theoretical, empirical, and clinical investigations of Loevinger’s conception of ego development (pp. 2738). Erlbaum.Google Scholar
McAdams, D. P. (2018). Narrative identity: What is it? What does it do? How do you measure it? Imagination, Cognition, and Personality, 37(3), 359372. https://doi.org/10.1177/0276236618756704CrossRefGoogle Scholar
McAdams, D. P., & McLean, K. C. (2013). Narrative identity. Current Directions in Psychological Science, 22(3), 233238. https://doi.org/10.1177/0963721413475622CrossRefGoogle Scholar
McLean, K. C., & Pasupathi, M. (2012). Processes of identity development: Where I am and how I got there. Identity, 12(1), 828. https://doi.org/10.1080/15283488.2011.632363CrossRefGoogle Scholar
McLean, K. C., Pasupathi, M., & Pals, J. L. (2007). Selves creating stories creating selves: A process model of self-development. Personality and Social Psychology Review, 11(3), 262278. https://doi.org/10.1177/1088868307301034CrossRefGoogle Scholar
Misra, S., & Stokols, D. (2012). Psychological and health outcomes of perceived information overload. Environment and Behavior, 44(6), 737759. https://doi.org/10.1177/0013916511404408CrossRefGoogle Scholar
Nesi, J., Choukas-Bradley, S., & Prinstein, M. J. (2018). Transformation of adolescent peer relations in the social media context: Part 2 – Application to peer group processes and future directions for research. Clinical Child and Family Psychology Review, 21(3), 295319. https://doi.org/10.1007/s10567–018-0262-9CrossRefGoogle ScholarPubMed
Nesi, J., Telzer, E. H., & Prinstein, M. J. (2020). Adolescent development in the digital media context. Psychological Inquiry, 31(3), 229234. https://doi.org/10.1080/1047840X.2020.1820219CrossRefGoogle ScholarPubMed
Pasupathi, M., & Hoyt, T. (2009). The development of narrative identity in late adolescence in emergent adulthood: The continued importance of listeners. Developmental Psychology, 45(2), 558574. https://doi.org/10.1037/a0014431CrossRefGoogle ScholarPubMed
Pasupathi, M., Mansour, E., & Brubaker, J. R. (2007). Developing a life story: Constructing relations between self and experience in autobiographical narratives. Human Development, 50(2–3), 85110. https://doi.org/10.1159/000100939CrossRefGoogle Scholar
Pasupathi, M., & Rich, B. (2005). Inattentive listening undermines self-verification in personal storytelling. Journal of Personality, 73(4), 10511085. https://doi.org/10.1111/j.1467-6494.2005.00338.xCrossRefGoogle ScholarPubMed
Peters, K. (2020, October 20). Web 2.0. Investopedia. https://www.investopedia.com/terms/w/web-20.aspGoogle Scholar
Rogers, C. R. (1961). On becoming a person: A therapist’s view of psychotherapy. Houghton Mifflin.Google Scholar
Sandoval-Almazan, R., & Gil-Garcia, J. R. (2014). Towards cyberactivism 2.0? Understanding the use of social media and other information technologies for political activism and social movements. Government Information Quarterly, 31(3), 365378. https://doi.org/10.1016/j.giq.2013.10.016CrossRefGoogle Scholar
Schwartz, S. H. (1996). Value priorities and behavior: Applying a theory of integrated value system. In Seligman, C., Olson, J. M., & Zanna, M. P. (Eds.), The Ontario Symposium: Vol. 8. The psychology of values (pp. 124). Erlbaum.Google Scholar
Seabrook, E. M., Kern, M. L., & Rickard, N. S. (2016). Social networking sites, depression, and anxiety: A systematic review. JMIR Mental Health, 3(4), Article e50. https://doi.org/10.2196/mental.5842CrossRefGoogle ScholarPubMed
Sebastian, C., Burnett, S., & Blakemore, S.-J. (2008). Development of the self-concept during adolescence. Trends in Cognitive Sciences, 12(11), 441446. https://doi.org/10.1016/j.tics.2008.07.008CrossRefGoogle ScholarPubMed
Singer, J. A. (2004). Narrative identity and meaning making across the life span: An introduction. Journal of Personality, 72(3), 437460. https://doi.org/10.1111/j.0022-3506.2004.00268.xCrossRefGoogle Scholar
Singer, J. A. (2020). Narrative identity in a digital age: What are the human risks? Psychological Inquiry, 31(3), 224228. https://doi.org/10.1080/1047840X.2020.1820217CrossRefGoogle Scholar
Singer, J. A., Blagov, P., Berry, M., & Oost, K. M. (2013). Self-defining memories, scripts, and the life story: Narrative identity in personality and psychotherapy. Journal of Personality, 81(6), 569582. https://doi.org/10.1111/jopy.12005CrossRefGoogle ScholarPubMed
Smith, C. (2013, August 5). Three out of four young adults delete social media posts over fears they could harm their careers. Business Insider. https://www.businessinsider.com/young-adults-delete-social-media-posts-2013-8?international=true&r=US&IR=TGoogle Scholar
Syed, M., & McLean, K. C. (2016). Understanding identity integration: Theoretical, methodological, and applied issues. Journal of Adolescence, 47, 109118. https://doi.org/10.1016/j.adolescence.2015.09.005CrossRefGoogle ScholarPubMed
Syed, M., & Seiffge-Krenke, I. (2015). Change in ego development, coping, and symptomatology from adolescence to emerging adulthood. Journal of Applied Developmental Psychology, 41, 110119. https://doi.org/10.1016/j.appdev.2015.09.003CrossRefGoogle Scholar
Taylor, J., & Pagliari, C. (2018). Mining social media data: How are research sponsors and researchers addressing the ethical challenges? Research Ethics, 14(2), 139. https://doi.org/10.1177/1747016117738559CrossRefGoogle Scholar
The School of Life (n.d.). How to live more wisely around our phones. https://www.theschooloflife.com/thebookoflife/how-to-live-more-wisely-around-our-phones/Google Scholar
Thorne, A. (2000). Personality memory telling and personality development. Personality and Social Psychology Review, 4(1), 4556. https://doi.org/10.1207/S15327957PSPR0401_5CrossRefGoogle ScholarPubMed
Valkenburg, P. M., Beyens, I., Pouwels, J. L., van Driel, I. I., & Keijsers, L. (2021). Social media and adolescents’ self-esteem: Heading for a person-specific media effects paradigm. Journal of Communication, 71(1), 5678. https://doi.org/10.1093/joc/jqaa039CrossRefGoogle Scholar
Van Doeselaar, L., McLean, K. C., Meeus, W., Denissen, J. J. A., & Klimstra, T. A. (2020). Adolescents’ identity formation: Linking the narrative and the dual-cycle approach. Journal of Youth and Adolescence, 49(4), 818835. https://doi.org/10.1007/s10964–019-01096-xCrossRefGoogle ScholarPubMed
Verduyn, P., Lee, D. S., Park, J., et al. (2015). Passive Facebook usage undermines affective well-being: Experimental and longitudinal evidence. Journal of Experimental Psychology: General, 144(2), 480488. https://doi.org/10.1037/xge0000057CrossRefGoogle ScholarPubMed
Vinh, N. (2021, February 10). Does counter culture still exist? The Western Front. https://www.westernfrononline.com/2021/02/10/does-counterculture-still-exist/Google Scholar
Westenberg, P. M., & Gjerde, P. F. (1999). Ego development during the transition from adolescence to young adulthood: A 9-year longitudinal study. Journal of Research in Personality, 33(2), 233252. https://doi.org/10.1006/jrpe.1999.2248CrossRefGoogle Scholar
Zaru, D. (2021, January 13). Trump Twitter ban raises concerns over ‘unchecked’ power of big tech. ABC News. https://abcnews.go.com/US/trump-twitter-ban-raises-concerns-unchecked-power-big/story?id=75150689Google Scholar
Zhong, C., Chang, H. W., Karamshuk, D., Lee, D., & Sastry, N. (2017, May 15–18). Wearing many (social) hats: How different are your different social network personae? Proceedings of the 11th International Conference on Web and Social Media, Montreal, QC, Canada.CrossRefGoogle Scholar

References

Allen, K. A., Ryan, T., Gray, D. L., McInerney, D. M., & Waters, L. (2014). Social media use and social connectedness in adolescents: The positives and the potential pitfalls. Australian Educational & Developmental Psychologist, 31(1), 1831. https://doi.org/10.1017/edp.2014.2CrossRefGoogle Scholar
Amichai-Hamburger, Y., & Ben-Artzi, E. (2003). Loneliness and internet use. Computers in Human Behavior, 19(1), 7180. https://doi.org/10.1016/S0747-5632(02)00014-6CrossRefGoogle Scholar
Anderson, M., & Jiang, J. (2018). Teens, social media & technology 2018. Pew Research Center. http://publicservicesalliance.org/wp-content/uploads/2018/06/Teens-Social-Media-Technology-2018-PEW.pdfGoogle Scholar
Bell, B. T. (2019). “You take fifty photos, delete forty nine and use one”: A qualitative study of adolescent image-sharing practices on social media. International Journal of Child-Computer Interaction, 20, 6471. https://doi.org/10.1016/j.ijcci.2019.03.002CrossRefGoogle Scholar
Bellmore, A. D., & Cillessen, A. H. (2006). Reciprocal influences of victimization, perceived social preference, and self-concept in adolescence. Self and Identity, 5(3), 209229. https://doi.org/10.1080/15298860600636647CrossRefGoogle Scholar
Boettner, B., Browning, C. R., & Calder, C. A. (2019). Feasibility and validity of geographically explicit ecological momentary assessment with recall‐aided space‐time budgets. Journal of Research on Adolescence, 29(3), 627645. https://doi.org/10.1111/jora.12474CrossRefGoogle ScholarPubMed
Boyle, S. C., Earle, A. M., LaBrie, J. W., & Ballou, K. (2017). Facebook dethroned: Revealing the more likely social media destinations for college students’ depictions of underage drinking. Addictive Behaviors, 65, 6367. https://doi.org/10.1016/j.addbeh.2016.10.004CrossRefGoogle ScholarPubMed
Boyle, S. C., LaBrie, J. W., Froidevaux, N. M., & Witkovic, Y. D. (2016). Different digital paths to the keg? How exposure to peers’ alcohol-related social media content influences drinking among male and female first-year college students. Addictive Behaviors, 57, 2129. https://doi.org/10.1016/j.addbeh.2016.01.011CrossRefGoogle Scholar
Bradley, G. L., & Inglis, B. C. (2012). Adolescent leisure dimensions, psychosocial adjustment, and gender effects. Journal of Adolescence, 35(5), 11671176. https://doi.org/10.1016/j.adolescence.2012.03.006CrossRefGoogle ScholarPubMed
Carrotte, E. R., Dietze, P. M., Wright, C. J., & Lim, M. S. (2016). Who ‘likes’ alcohol? Young Australians’ engagement with alcohol marketing via social media and related alcohol consumption patterns. Australian and New Zealand Journal of Public Health, 40(5), 474479. https://doi.org/10.1111/1753-6405.12572CrossRefGoogle ScholarPubMed
Chein, J., Albert, D., O’Brien, L., Uckert, K., & Steinberg, L. (2011). Peers increase adolescent risk taking by enhancing activity in the brain’s reward circuitry. Developmental Science, 14(2), F1F10. https://doi.org/10.1111/j.1467-7687.2010.01035.xCrossRefGoogle ScholarPubMed
Choukas-Bradley, S., Nesi, J., Widman, L., & Higgins, M. K. (2019). Camera-ready: Young women’s appearance-related social media consciousness. Psychology of Popular Media Culture, 8(4), 473481. https://doi.org/10.1037/ppm0000196CrossRefGoogle Scholar
Davis, K. (2012). Friendship 2.0: Adolescents’ experiences of belonging and self-disclosure online. Journal of Adolescence, 35, 15271536. https://doi.org/10.1016/j.adolescence.2012.02.013CrossRefGoogle ScholarPubMed
de Vries, D. A., Möller, A. M., Wieringa, M. S., Eigenraam, A. W., & Hamelink, K. (2018). Social comparison as the thief of joy: Emotional consequences of viewing strangers’ Instagram posts. Media Psychology, 21(2), 222245. https://doi.org/10.1080/15213269.2016.1267647CrossRefGoogle Scholar
Domahidi, E. (2018). The associations between online media use and users’ perceived social resources: A meta-analysis. Journal of Computer-Mediated Communication, 23(4), 181200. https://doi.org/10.1093/jcmc/zmy007CrossRefGoogle Scholar
Duvenage, M., Uink, B. N., Zimmer‐Gembeck, M. J., Barber, B. L., Donovan, C. L., & Modecki, K. L. (2019). Ambulatory assessment of adolescent coping: It’s a complicated process. Journal of Research on Adolescence, 29(3), 578594. https://doi.org/10.1111/jora.12468CrossRefGoogle ScholarPubMed
Ehrenreich, S. E., Beron, K. J., Burnell, K., Meter, D. J., & Underwood, M. K. (2020). How adolescents use text messaging through their high school years. Journal of Research on Adolescence, 30(2), 521540. https://doi.org/10.1111/jora.12541CrossRefGoogle ScholarPubMed
Ehrenreich, S. E., George, M., Burnell, K., & Underwood, M. K. (2021). Importance of digital communication in adolescents’ development: Theoretical and empirical advancements in the last decade. Journal of Research on Adolescence, 31(4), 928943. https://doi.org/10.1111/jora.12643CrossRefGoogle ScholarPubMed
Ehrenreich, S. E., Underwood, M. K., & Ackerman, R. A. (2014). Adolescents’ text message communication and growth in antisocial behavior across the first year of high school. Journal of Abnormal Child Psychology, 42(2), 251264. https://doi.org/10.1007/s10802-013-9783-3CrossRefGoogle ScholarPubMed
Elkind, D. (1967). Egocentrism in adolescence. Child Development, 38(4), 10251034. https://doi.org/10.2307/1127100CrossRefGoogle ScholarPubMed
Erikson, E. (1968). Identity: Youth and crises. W. W. Norton & Company.Google Scholar
Fardouly, J., & Vartanian, L. R. (2016). Social media and body image concerns: Current research and future directions. Current Opinion in Psychology, 9, 15. https://doi.org/10.1016/j.copsyc.2015.09.005CrossRefGoogle Scholar
Frison, E., Bastin, M., Bijttebier, P., & Eggermont, S. (2019). Helpful or harmful? The different relationships between private Facebook interactions and adolescents’ depressive symptoms. Media Psychology, 22(2), 244272. https://doi.org/10.1080/15213269.2018.1429933CrossRefGoogle Scholar
Frison, E., & Eggermont, S. (2015). Toward an integrated and differential approach to the relationships between loneliness, different types of Facebook use, and adolescents’ depressed mood. Communication Research, 47(5), 701728. https://doi.org/10.1177/0093650215617506CrossRefGoogle Scholar
George, M. J., Beron, K., Vollet, J., Burnell, K., Ehrenreich, S. E., & Underwood, M. K. (2021). Frequency of text messaging and adolescents’ mental health symptoms across four years of high school. JAMA Pediatrics, 68(2), 324330. https://doi.org/10.1016/j.jadohealth.2020.06.012Google Scholar
George, M. J., Rivenbark, J. G., Russell, M. A., Ng’eno, L., Hoyle, R. H., & Odgers, C. L. (2019). Evaluating the use of commercially available wearable wristbands to capture adolescents’ daily sleep duration. Journal of Research on Adolescence, 29(3), 613626. https://doi.org/10.1111/jora.12467CrossRefGoogle ScholarPubMed
Geusens, F., & Beullens, K. (2017a). Strategic self-presentation or authentic communication? Predicting adolescents’ alcohol references on social media. Journal of Studies on Alcohol and Drugs, 78(1), 124133. https://doi.org/10.15288/jsad.2017.78.124CrossRefGoogle ScholarPubMed
Geusens, F., & Beullens, K. (2017b). The reciprocal associations between sharing alcohol references on social networking sites and binge drinking: A longitudinal study among late adolescents. Computers in Human Behavior, 73, 499506. https://doi.org/10.1016/j.chb.2017.03.062CrossRefGoogle Scholar
große Deters, F., & Mehl, M. R. (2013). Does posting Facebook status updates increase or decrease loneliness? An online social networking experiment. Social Psychological and Personality Science, 4(5), 579586. https://doi.org/10.1177/1948550612469233CrossRefGoogle Scholar
Hanneman, R. A., & Riddle, M. (2011). Concepts and measure for basic network analysis. In Scott, J. & Carrington, P. J. (Eds.), The Sage handbook of social network analysis (pp. 364367). Sage.Google Scholar
Harter, S., Stocker, C., & Robinson, N. S. (1996). The perceived directionality of the link between approval and self-worth: The liabilities of a looking glass self-orientation among young adolescents. Journal of Research on Adolescence, 6(3), 285308.Google Scholar
Hendriks, H., Van den Putte, B., Gebhardt, W. A., & Moreno, M. A. (2018). Social drinking on social media: Content analysis of the social aspects of alcohol-related posts on Facebook and Instagram. Journal of Medical Internet Research, 20(6), e226. https://doi.org/10.2196/jmir.9355CrossRefGoogle ScholarPubMed
Huang, G. C., Unger, J. B., Soto, D., et al. (2014). Peer influences: The impact of online and offline friendship networks on adolescent smoking and alcohol use. Journal of Adolescent Health, 54(5), 508514. https://doi.org/10.1016/j.jadohealth.2013.07.001CrossRefGoogle ScholarPubMed
Hunt, M. G., Marx, R., Lipson, C., & Young, J. (2018). No more FOMO: Limiting social media decreases loneliness and depression. Journal of Social & Clinical Psychology, 37(10), 751768. https://doi.org/10.1521/jscp.2018.37.10.751CrossRefGoogle Scholar
Kallgren, C. A., Reno, R. R., & Cialdini, R. B. (2000). A focus theory of normative conduct: When norms do and do not affect behavior. Personality and Social Psychology Bulletin, 26(8), 10021012. https://doi.org/10.1177/01461672002610009CrossRefGoogle Scholar
Kowalski, R. M., Giumetti, G. W., Schroeder, A. N., & Lattaner, M. R. (2014). Bullying in the digital age: A critical review and meta-analysis of cyberbullying among youth. Psychological Bulletin, 140(4), 10721137. https://doi.org/10.1037/a0035618CrossRefGoogle Scholar
Kraut, R., Patterson, M., Lundmark, V., Kiesler, S., Mukophadhyay, T., & Scherlis, W. (1998). Internet paradox: A social technology that reduces social involvement and psychological well-being? American Psychologist, 53(9), 10171031. https://doi.org/10.1037/0003-066X.53.9.1017CrossRefGoogle ScholarPubMed
Kraut, R., Kiesler, S., Boneva, B., Cummings, J., Helgeson, V., & Crawford, A. (2002). Internet paradox revisited. Journal of Social Issues, 58(1), 4974. https://doi.org/10.1111/1540-4560.00248CrossRefGoogle Scholar
Kross, E., Verduyn, P., Demiralp, E., et al. (2013). Facebook use predicts declines in subjective well-being in young adults. PLoS ONE, 8(8), Article e69841. https://doi.org/10.1371/journal.pone.0069841CrossRefGoogle ScholarPubMed
La Greca, A. M., & Harrison, H. M. (2005). Adolescent peer relations, friendships, and romantic relationships: Do they predict social anxiety and depression?. Journal of Clinical Child and Adolescent Psychology, 34(1), 4961. https://doi.org/10.1207/s15374424jccp3401_5CrossRefGoogle ScholarPubMed
Lenhart, A. (2015, April 9). Teens, social media and technology overview 2015. Pew Research Center. http://www.pewinternet.org/2015/04/09/teens-social-media-technology-2015/Google Scholar
Lenhart, A., Ling, R., Campbell, S., & Purcell, K. (2010). Teens and mobile phones. Pew Research Center. http://pewinternetorg/Reports/2010/Teens-and-Mobile-Phones.aspxGoogle Scholar
Ling, R. (2005). Mobile communications vis-à-vis teen emancipation, peer group integration, and deviance. In Harper, R., Palen, L., & Taylor, A. (Eds.), The inside text: Social, cultural, and design perspectives on SMS (pp. 175193). Springer.CrossRefGoogle Scholar
Lou, L. L., Yan, Z., Nickerson, A., & McMorris, R. (2012). An examination of the reciprocal relationship of loneliness and Facebook use among first-year college students. Journal of Educational Computing Research, 46(1), 105117. https://doi.org/10.2190/EC.46.1.eCrossRefGoogle Scholar
Madden, M., Lenhart, A., Cortesi, S., et al. (2013, May 21). Teens, social media, and privacy. Pew Research Center. http://www.pewinternet.org/2013/05/21/teens-social-media-and-privacy/Google Scholar
Marwick, A. E. (2013). Status update: Celebrity, publicity, and branding in the social media age. Yale University Press.Google Scholar
Marwick, A. E. (2015). Instafame: Luxury selfies in the attention economy. Public Culture, 27(75), 137160. https://doi.org/10.1215/08992363-2798379CrossRefGoogle Scholar
Matook, S., Cummings, J., & Bala, H. (2015). Are you feeling lonely? The impact of relationship characteristics and online social network features on loneliness. Journal of Management Information Systems, 31(4), 278310. https://doi.org/10.1080/07421222.2014.1001282CrossRefGoogle Scholar
Meter, D. J., Ehrenreich, S. E., Carker, C., Flynn, E., & Underwood, M. K. (2019). Older adolescents’ understanding of participant rights in the BlackBerry Project, a longitudinal ambulatory assessment study. Journal of Research on Adolescence, 29(3), 662674. https://doi.org/10.1111/jora.12461CrossRefGoogle Scholar
Moewaka Barnes, H., McCreanor, T., Goodwin, I., Lyons, A., Griffin, C., & Hutton, F. (2016). Alcohol and social media: Drinking and drunkenness while online. Critical Public Health, 26(1), 6276. https://doi.org/10.1080/09581596.2015.1058921CrossRefGoogle Scholar
Mojtabai, R., Olfson, M., & Han, B. (2016). National trends in the prevalence and treatment of depression in adolescents and young adults. Pediatrics, 138(6), Article 320161878. https://doi.org/10.1542/peds.2016-1878CrossRefGoogle ScholarPubMed
Morgan, E. M., Snelson, C., & Elison-Bowers, P. (2010). Image and video disclosure of substance use on social media websites. Computers in Human Behavior, 26(6), 14051411. https://doi.org/10.1016/j.chb.2010.04.017CrossRefGoogle Scholar
Nesi, J., Choukas-Bradley, S., & Prinstein, M. J. (2018a). Transformation of adolescent peer relations in the social media context: Part 1 – A theoretical framework and application to dyadic peer relationships. Clinical Child and Family Psychology Review, 21(3), 267294. https://doi.org/10.1007/s10567-018-0261-xCrossRefGoogle ScholarPubMed
Nesi, J., Choukas-Bradley, S., & Prinstein, M. J. (2018b). Transformation of adolescent peer relations in the social media context: Part 2 – Application to peer group processes and future directions for research. Clinical Child and Family Psychology Review, 21(3), 295319. https://doi.org/10.1007/s10567-018-0262-9CrossRefGoogle ScholarPubMed
Nesi, J., & Prinstein, M. J. (2019). In search of likes: Longitudinal associations between adolescents’ digital status seeking and health-risk behaviors. Journal of Clinical Child and Adolescent Psychology, 48(5), 740748. https://doi.org/10.1080/15374416.2018.1437733CrossRefGoogle ScholarPubMed
Nesi, J., Rothenberg, W. A., Hussong, A. M., & Jackson, K. M. (2017). Friends’ alcohol-related social networking site activity predicts escalations in adolescent drinking: Mediation by peer norms. Journal of Adolescent Health, 60(6), 641647. https://doi.org/10.1016/j.jadohealth.2017.01.009CrossRefGoogle ScholarPubMed
Newman, B. M., Lohman, B. J., & Newman, P. R. (2007). Peer group membership and a sense of belonging: Their relationship to adolescent behavior problems. Adolescence, 42(166), 241263. http://www.ncbi.nlm.nih.gov/pubmed/17849935Google Scholar
O’Brien, L., Albert, D., Chein, J., & Steinberg, L. (2011). Adolescents prefer more immediate rewards when in the presence of their peers. Journal of Research on Adolescence, 21(4), 747753. https://doi.org/10.1111/j.1532-7795.2011.00738.xCrossRefGoogle Scholar
Piehler, T. F., & Dishion, T. J. (2007). Interpersonal dynamics within adolescent friendships: Dyadic mutuality, deviant talk, and patterns of antisocial behavior. Child Development, 78(5), 16111624. https://doi.org/10.1111/j.1467-8624.2007.01086.xCrossRefGoogle ScholarPubMed
Pittman, M., & Reich, B. (2016). Social media and loneliness: Why an Instagram picture may be worth more than a thousand Twitter words. Computers in Human Behavior, 62, 155167. https://doi.org/10.1016/j.chb.2016.03.084CrossRefGoogle Scholar
Primack, B. A., Shensa, A., Sidani, J. E., et al. (2017). Social media use and perceived social isolation among young adults in the U.S. American Journal of Preventative Medicine, 53(1), 18. https://doi.org/10.1016/j.amepre.2017.01.010CrossRefGoogle ScholarPubMed
Prinstein, M. J., & Dodge, K. A. (2008). Understanding peer influence in children and adolescents. Guilford Press.Google Scholar
Ranney, J. D., & Troop-Gordon, W. (2020). The role of popularity and digital self-monitoring in adolescents’ cyberbehaviors and cybervictimization. Computers in Human Behavior, 102, 293302. https://doi.org/10.1016/j.chb.2019.08.023CrossRefGoogle Scholar
Reich, S. M. (2017). Connecting offline social competence to online peer interactions. Psychology of Popular Media Culture, 6(4), 291310. https://doi.org/10.1037/ppm0000111CrossRefGoogle Scholar
Rideout, V., & Robb, M. B. (2018). Social media, social life: Teens reveal their experiences. Common Sense Media. https://www.commonsensemedia.org/research/social-media-social-life-2018Google Scholar
Rui, J. R., & Stefanone, M. A. (2016). The desire for fame: An extension of uses and gratifications theory. Communication Studies, 67(4), 399418. https://doi.org/10.1080/10510974.2016.1156006CrossRefGoogle Scholar
Sherman, L. E., Hernandez, L. M., Greenfield, P. M., & Dapretto, M. (2018). What the brain ‘likes’: Neural correlates of providing feedback on social media. Social Cognitive and Affective Neuroscience, 13(7), 699707. https://doi.org/10.1093/scan/nsy051CrossRefGoogle ScholarPubMed
Sherman, L. E., Greenfield, P. M., Hernandez, L. M., & Dapretto, M. (2018). Peer influence via instagram: Effects on brain and behavior in adolescence and young adulthood. Child Development, 89(1), 3747. https://doi.org/10.1111/cdev.12838CrossRefGoogle ScholarPubMed
Sherman, L. E., Payton, A. A., Hernandez, L. M., Greenfield, P. M., & Dapretto, M. (2016). The power of the like in adolescence: Effects of peer influence on neural and behavioral responses to social media. Psychological Science, 27(7), 10271035. https://doi.org/10.1177/0956797616645673CrossRefGoogle ScholarPubMed
Siriaraya, P., Tang, C., Ang, C. S., Pfeil, U., & Zaphiris, P. (2011). A comparison of empathic communication pattern for teenagers and older people in online support communities. Behaviour & Information Technology, 30(5), 617628. https://doi.org/10.1080/0144929X.2011.582146CrossRefGoogle Scholar
Snyder, J., McEachern, A., Schrepferman, L., et al. (2010). Contribution of peer deviancy training to the early development of conduct problems: Mediators and moderators. Behavior Therapy, 41(3), 317328. https://doi.org/10.1016/j.beth.2009.05.001CrossRefGoogle Scholar
Somerville, L. H. (2013). The teenage brain sensitivity to social evaluation. Current Directions in Psychological Science, 22(2), 121127. https://doi.org/10.1177/0963721413476512CrossRefGoogle ScholarPubMed
Song, H., Hayeon, S., Anne, Z. S., et al. (2014). Does Facebook make you lonely? A meta analysis. Computers in Human Behavior, 36, 446452. https://doi.org/10.1016/j.chb.2014.04.011CrossRefGoogle Scholar
Steinberg, L. (2008). A social neuroscience perspective on adolescent risk-taking. Developmental Review, 28(1), 78106. https://doi.org/10.1016/j.dr.2007.08.002CrossRefGoogle ScholarPubMed
Steinberg, L., & Monahan, K. C. (2007). Age differences in resistance to peer influence. Developmental Psychology, 43(6), 15311543. https://doi.org/10.1037/0012-1649.43.6.1531CrossRefGoogle ScholarPubMed
Steinsbekk, S., Wichstrøm, L., Stenseng, F., Nesi, J., Hygen, B. W., & Skalická, V. (2021). The impact of social media use on appearance self-esteem from childhood to adolescence: A 3-wave community study. Computers in Human Behavior, 114, Article 106528. https://doi.org/10.1016/j.chb.2020.106528CrossRefGoogle Scholar
Subrahmanyam, K., Reich, S. M., Waechter, N., & Espinoza, G. (2008). Online and offline social networks: Use of social networking sites by emerging adults. Journal of Applied Developmental Psychology, 29(6), 420433. https://doi.org/10.1016/j.appdev.2008.07.003CrossRefGoogle Scholar
Subrahmanyam, K., Smahel, D., & Greenfield, P. (2006). Connecting developmental constructions to the internet: Identity presentation and sexual exploration in online teen chat rooms. Developmental Psychology, 42(3), 395406. https://doi.org/10.1037/0012-1649.42.3.395CrossRefGoogle Scholar
Swirsky, J. M., Rosie, M., & Xie, H. (2021). Adjustment correlates of social media engagement among early adolescents. Journal of Youth and Adolescence, 50, 22652278. https://doi.org/10.1007/s10964-021-01421-3CrossRefGoogle ScholarPubMed
Tromholt, M. (2016). The Facebook experiment: Quitting Facebook leads to higher levels of well-being. Cyberpsychology, Behavior, and Social Networking, 19(11), 661666. https://doi.org/10.1089/cyber.2016.0259CrossRefGoogle ScholarPubMed
Turkle, S. (2012). Alone together: Why we expect more from technology and less from each other. Basic Books.Google Scholar
Twenge, J. M. (2019). More time on technology, less happiness? Associations between digital-media use and psychological well-being. Current Directions in Psychological Science, 28(4), 372379. https://doi.org/10.1177/0963721419838244CrossRefGoogle Scholar
Twenge, J. M., Joiner, T. E., Rogers, M. L., & Martin, G. N. (2018). Increases in depressive symptoms, suicide-related outcomes, and suicide rates among U.S. adolescents after 2010 and links to increased new media screen time. Clinical Psychological Science, 6(1), 317. https://doi.org/10.1177/2167702617723376CrossRefGoogle Scholar
Uhls, Y. T., & Greenfield, P. M. (2012). The value of fame: Preadolescent perceptions of popular media and their relationship to future aspirations. Developmental Psychology, 48(2), 315326. https://doi.org/10.1037/a0026369CrossRefGoogle ScholarPubMed
Uhls, Y. T., Zgourou, E., & Greenfield, P. M. (2014). 21st century media, fame, and other future aspirations: A national survey of 9–15 year olds. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 8(4). https://doi.org/10.5817/CP2014-4-5CrossRefGoogle Scholar
Underwood, M. K., Brown, B. B., & Ehrenreich, S. E. (2018). Social media and peer relations. In Rubin, K. H., Bukowski, W. M., & Laursen, B. (Eds.), Handbook of peer interactions, relationships, and groups (2nd ed.; pp. 533551). Guilford Press.Google Scholar
Underwood, M. K., Rosen, L. H., More, D., Ehrenreich, S. E., & Gentsch, J. K. (2012). The BlackBerry project: Capturing the content of adolescents’ text messaging. Developmental Psychology, 48(2), 295302. https://doi.org/10.1037/a0025914CrossRefGoogle ScholarPubMed
Valkenburg, P. M., & Peter, J. (2007). Preadolescents’ and adolescents’ online communication and their closeness to friends. Developmental Psychology, 43(2), 267277. https://doi.org/10.1037/0012-1649.43.2.267CrossRefGoogle ScholarPubMed
Vannucci, A., & McCauley Ohannessian, C. M. (2019). Social media use subgroups differentially predict psychosocial well-being during early adolescence. Journal of Youth and Adolescence, 48(8), 14691493. https://doi.org/10.1007/s10964-019-01060-9CrossRefGoogle ScholarPubMed
Vogel, E. A., Rose, J. P., Okdie, B. M., Eckles, K., & Franz, B. (2015). Who compares and despairs? The effect of social comparison orientation on social media use and its outcomes. Personality and Individual Differences, 86, 249256. https://doi.org/10.1016/j.paid.2015.06.026CrossRefGoogle Scholar
Wang, C., (2020, June 7). Why TikTok made its user so obsessive? The AI algorithm that got you hooked. Towards Data Science. https://towardsdatascience.com/why-tiktok-made-its-user-so-obsessive-the-ai-algorithm-that-got-you-hooked-7895bb1ab423Google Scholar
Weinstein, E. (2017). Adolescents’ differential responses to social media browsing: Exploring causes and consequences for intervention. Computers in Human Behavior, 76, 396405. https://doi.org/10.1016/j.chb.2017.07.038CrossRefGoogle Scholar
Wentzel, K. R., Jablansky, S., & Scalise, N. R. (2021). Peer social acceptance and academic achievement: A meta-analytic study. Journal of Educational Psychology, 113(1), 157180. https://doi.org/10.1037/edu0000468CrossRefGoogle Scholar
Wright, M. F., & Li, Y. (2011). The associations between young adults’ face-to-face prosocial behaviors and their online prosocial behaviors. Computers in Human Behavior, 27, 19591962. https://doi.org/10.1016/j.chb.2011.04.019CrossRefGoogle Scholar
Yau, J. C., & Reich, S. M. (2019). “It’s just a lot of work”: Adolescents’ self‐presentation norms and practices on Facebook and Instagram. Journal of Research on Adolescence, 29(1), 196209. https://doi.org/10.1111/jora.12376CrossRefGoogle ScholarPubMed
Yoo, W., Yang, J., & Cho, E. (2016). How social media influence college students’ smoking attitudes and intentions. Computers in Human Behavior, 64, 173182. https://doi.org/10.1016/j.chb.2016.06.061CrossRefGoogle ScholarPubMed

References

Adolphs, R. (2009). The social brain: Neural basis of social knowledge. Annual Review of Psychology, 60(1), 693716.CrossRefGoogle ScholarPubMed
Alloway, T. P., & Alloway, R. G. (2012). The impact of engagement with social networking sites (SNSs) on cognitive skills. Computers in Human Behavior, 28(5), 17481754. https://doi.org/10.1016/j.chb.2012.04.015CrossRefGoogle Scholar
Alloway, T. P., Horton, J., Alloway, R. G., & Dawson, C. (2013). Social networking sites and cognitive abilities: Do they make you smarter? Computers & Education, 63, 1016. https://doi.org/10.1016/j.compedu.2012.10.030CrossRefGoogle Scholar
Atzil, S., Gao, W., Fradkin, I., & Barrett, L. F. (2018). Growing a social brain. Nature Human Behaviour, 2(9), 624636. https://doi.org/10.1038/s41562-018-0384-6CrossRefGoogle ScholarPubMed
Baek, E. C., Scholz, C., O’Donnell, M. B., & Falk, E. B. (2017). The value of sharing information: A neural account of information transmission. Psychological Science, 28(7), 851861. https://doi.org/10.1177/0956797617695073CrossRefGoogle ScholarPubMed
Bartra, O., McGuire, J. T., & Kable, J. W. (2013). The valuation system: A coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value. Neuroimage, 76, 412427. https://doi.org/10.1016/j.neuroimage.2013.02.063CrossRefGoogle ScholarPubMed
Baumgartner, S. E., Weeda, W. D., van der Heijden, L. L., & Huizinga, M. (2014). The relationship between media multitasking and executive function in early adolescents. The Journal of Early Adolescence, 34(8), 11201144. https://doi.org/10.1177/0272431614523133CrossRefGoogle Scholar
Becht, A. I., Wierenga, L. M., Mills, K. L., et al. (2021). Beyond the average brain: Individual differences in social brain development are associated with friendship quality. Social Cognitive and Affective Neuroscience, 16(3), 292301. https://doi.org/10.1093/scan/nsaa166CrossRefGoogle ScholarPubMed
Bennett, B. (2017). The internet is destroying society, sobering research shows. CNET.Google Scholar
Bhanji, J. P., & Delgado, M. R. (2014). The social brain and reward: Social information processing in the human striatum. Wiley Interdisciplinary Reviews: Cognitive Science, 5(1), 6173. https://doi.org/10.1002/wcs.1266Google ScholarPubMed
Bickart, K. C., Wright, C. I., Dautoff, R. J., Dickerson, B. C., & Barrett, L. F. (2011). Amygdala volume and social network size in humans. Nature Neuroscience, 14, 163164. https://doi.org/10.1038/nn.2724CrossRefGoogle ScholarPubMed
Blakemore, S.-J. (2008). The social brain in adolescence. Nature Reviews Neuroscience, 9, 267277. https://doi.org/10.1038/nrn2353CrossRefGoogle ScholarPubMed
Blakemore, S.-J. (2012). Development of the social brain in adolescence. Journal of the Royal Society of Medicine, 105(3), 111116. https://doi.org/10.1258/jrsm.2011.110221CrossRefGoogle ScholarPubMed
Blakemore, S.-J. (2018). Avoiding social risk in adolescence. Current Directions in Psychological Science, 27(2), 116122. https://doi.org/10.1177/0963721417738144CrossRefGoogle Scholar
Blakemore, S.-J., & Robbins, T. W. (2012). Decision-making in the adolescent brain. Nature Neuroscience, 15, 11841191. https://doi.org/10.1038/nn.3177CrossRefGoogle ScholarPubMed
Braams, B. R., Peters, S., Peper, J. S., Güroğlu, B., & Crone, E. A. (2014). Gambling for self, friends, and antagonists: Differential contributions of affective and social brain regions on adolescent reward processing. Neuroimage, 100, 281289. https://doi.org/10.1016/j.neuroimage.2014.06.020CrossRefGoogle ScholarPubMed
Brand, M., Young, K. S., & Laier, C. (2014). Prefrontal control and internet addiction: A theoretical model and review of neuropsychological and neuroimaging findings. Frontiers in Human Neuroscience, 8, Article 375. https://doi.org/10.3389/fnhum.2014.00375CrossRefGoogle ScholarPubMed
Cai, Y., Li, S., Liu, J., et al. (2016). The role of the frontal and parietal cortex in proactive and reactive inhibitory control: A transcranial direct current stimulation study. Journal of Cognitive Neuroscience, 28(1), 177186. https://doi.org/10.1162/jocn_a_00888CrossRefGoogle ScholarPubMed
Cascio, C. N., O’Donnell, M. B., Bayer, J., Tinney, F. J., & Falk, E. B. (2015). Neural correlates of susceptibility to group opinions in online word-of-mouth recommendations. Journal of Marketing Research, 52(4), 559575. https://doi.org/10.1509/jmr.13.0611CrossRefGoogle Scholar
Chein, J. M., & Schneider, W. (2005). Neuroimaging studies of practice-related change: fMRI and meta-analytic evidence of a domain-general control network for learning. Cognitive Brain Research, 25(3), 607623. https://doi.org/10.1016/j.cogbrainres.2005.08.013CrossRefGoogle ScholarPubMed
Chen, J., Liang, Y., Mai, C., Zhong, X., & Qu, C. (2016). General deficit in inhibitory control of excessive smartphone users: Evidence from an event-related potential study. Frontiers in Psychology, 7, Article 511. https://doi.org/10.3389/fpsyg.2016.00511CrossRefGoogle ScholarPubMed
Choi, J.-S., Park, S. M., Roh, M.-S., et al. (2014). Dysfunctional inhibitory control and impulsivity in internet addiction. Psychiatry Research, 215(2), 424428. https://doi.org/10.1016/j.psychres.2013.12.001CrossRefGoogle ScholarPubMed
Chun, J.-W., Choi, J., Cho, H., et al. (2018). Role of frontostriatal connectivity in adolescents with excessive smartphone use. Frontiers in Psychiatry, 9, Article 437. https://doi.org/10.3389/fpsyt.2018.00437CrossRefGoogle ScholarPubMed
Chun, J.-W., Park, C.-H., Kim, J.-Y., et al. (2020). Altered core networks of brain connectivity and personality traits in internet gaming disorder. Journal of Behavioral Addictions, 9(2), 298311. https://doi.org/10.1556/2006.2020.00014CrossRefGoogle ScholarPubMed
Cole, M. W., & Schneider, W. (2007). The cognitive control network: Integrated cortical regions with dissociable functions. Neuroimage, 37(1), 343360. https://doi.org/10.1016/j.neuroimage.2007.03.071CrossRefGoogle ScholarPubMed
Coyne, S. M., Rogers, A. A., Zurcher, J. D., Stockdale, L., & Booth, M. (2020). Does time spent using social media impact mental health?: An eight year longitudinal study. Computers in Human Behavior, 104, Article 106160. https://doi.org/10.1016/j.chb.2019.106160CrossRefGoogle Scholar
Decety, J., & Lamm, C. (2006). Human empathy through the lens of social neuroscience. The Scientific World Journal, 6, 11461163. https://doi.org/10.1100/tsw.2006.221CrossRefGoogle ScholarPubMed
Ding, W., Sun, J., Sun, Y., et al. (2013). Altered default network resting-state functional connectivity in adolescents with internet gaming addiction. PLoS ONE, 8(3), e59902. https://doi.org/10.1371/journal.pone.0059902CrossRefGoogle ScholarPubMed
Dixon, M. L., De La Vega, A., Mills, C., et al. (2018). Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks. Proceedings of the National Academy of Sciences, 115(7), E1598E1607. https://doi.org/10.1073/pnas.1715766115CrossRefGoogle Scholar
Dong, G., Zhou, H., & Zhao, X. (2011). Male internet addicts show impaired executive control ability: Evidence from a color-word Stroop task. Neuroscience Letters, 499(2), 114118. https://doi.org/10.1016/j.neulet.2011.05.047CrossRefGoogle ScholarPubMed
Dosenbach, N. U. F., Fair, D. A., Cohen, A. L., Schlaggar, B. L., & Petersen, S. E. (2008). A dual-networks architecture of top-down control. Trends in Cognitive Sciences, 12(3), 99105. https://doi.org/10.1016/j.tics.2008.01.001CrossRefGoogle ScholarPubMed
Duven, E. C. P., Müller, K. W., Beutel, M. E., & Wölfling, K. (2015). Altered reward processing in pathological computer gamers: ERP‐results from a semi‐natural gaming‐design. Brain and Behavior, 5(1), e00293. https://doi.org/10.1002/brb3.293CrossRefGoogle ScholarPubMed
Engelberg, E., & Sjöberg, L. (2004). Internet use, social skills, and adjustment. Cyberpsychology & Behavior, 7(1), 4147. https://doi.org/10.1089/109493104322820101CrossRefGoogle ScholarPubMed
Feng, Q., Chen, X., Sun, J., et al. (2013). Voxel-level comparison of arterial spin-labeled perfusion magnetic resonance imaging in adolescents with internet gaming addiction. Behavioral and Brain Functions, 9(1), 111. https://doi.org/10.1186/1744-9081-9-33CrossRefGoogle ScholarPubMed
Figner, B., Mackinlay, R. J., Wilkening, F., & Weber, E. U. (2009). Affective and deliberative processes in risky choice: Age differences in risk taking in the Columbia Card Task. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(3), 709730. https://doi.org/10.1037/a0014983Google ScholarPubMed
Firth, J., Torous, J., Stubbs, B., et al. (2019). The “online brain”: How the internet may be changing our cognition. World Psychiatry, 18(2), 119129. https://doi.org/10.1002/wps.20617CrossRefGoogle ScholarPubMed
Gao, L., Zhang, J., Xie, H., Nie, Y., Zhao, Q., & Zhou, Z. (2020). Effect of the mobile phone related-background on inhibitory control of problematic mobile phone use: An event-related potentials study. Addictive Behaviors, 108, Article 106363. https://doi.org/10.1016/j.addbeh.2020.106363CrossRefGoogle ScholarPubMed
Gao, Q., Jia, G., Zhao, J., & Zhang, D. (2019). Inhibitory control in excessive social networking users: Evidence from an ERP-based Go-Nogo task. Frontiers in Psychology, 10, Article 1810. https://doi.org/10.3389/fpsyg.2019.01810CrossRefGoogle Scholar
Gilmore, C. S., Dickmann, P. J., Nelson, B. G., Lamberty, G. J., & Lim, K. O. (2018). Transcranial direct current stimulation (tDCS) paired with a decision-making task reduces risk-taking in a clinically impulsive sample. Brain Stimulation, 11(2), 302309. https://doi.org/10.1016/j.brs.2017.11.011CrossRefGoogle Scholar
Gindrat, A.-D., Chytiris, M., Balerna, M., Rouiller, E. M., & Ghosh, A. (2015). Use-dependent cortical processing from fingertips in touchscreen phone users. Current Biology, 25(1), 109116. https://doi.org/10.1016/j.cub.2014.11.026CrossRefGoogle ScholarPubMed
Gogtay, N., & Thompson, P. M. (2010). Mapping gray matter development: Implications for typical development and vulnerability to psychopathology. Brain and Cognition, 72(1), 615. https://doi.org/10.1016/j.bandc.2009.08.009CrossRefGoogle ScholarPubMed
Gratton, C., Sun, H., & Petersen, S. E. (2018). Control networks and hubs. Psychophysiology, 55(3), e13032. https://doi.org/10.1111/psyp.13032CrossRefGoogle ScholarPubMed
Grieve, R., Indian, M., Witteveen, K., Anne Tolan, G., & Marrington, J. (2013). Face-to-face or Facebook: Can social connectedness be derived online? Computers in Human Behavior, 29(3), 604609. https://doi.org/10.1016/j.chb.2012.11.017CrossRefGoogle Scholar
Griffiths, M. D., Kuss, D. J., & Demetrovics, Z. (2014). Social networking addiction: An overview of preliminary findings. In Rosenberg, K. P. & Curtiss Feder, L. (Eds.), Behavioral addictions: Criteria, evidence, and treatment (pp. 119141). Elsevier Academic Press. https://doi.org/10.1016/B978–0-12-407724-9.00006-9CrossRefGoogle Scholar
Gutiérrez, J. D. S., de Fonseca, F. R., & Rubio, G. (2016). Cell-phone addiction: A review. Frontiers in Psychiatry, 7, Article 175. https://doi.org/10.3389/fpsyt.2016.00175Google Scholar
Hadar, A., Hadas, I., Lazarovits, A., Alyagon, U., Eliraz, D., & Zangen, A. (2017). Answering the missed call: Initial exploration of cognitive and electrophysiological changes associated with smartphone use and abuse. PLoS ONE, 12(7), e0180094. https://doi.org/10.1371/journal.pone.0180094CrossRefGoogle ScholarPubMed
Harris, T. (2016, May 18). How technology hijacks people’s minds – from a magician and Google’s design ethicist. Medium Magazine. https://medium.com/thrive-global/how-technology-hijacks-peoples-minds-from-a-magician-and-google-s-design-ethicist-56d62ef5edf3Google Scholar
He, Q., Turel, O., & Bechara, A. (2017). Brain anatomy alterations associated with social networking site (SNS) addiction. Scientific Reports, 7, Article 45064. https://doi.org/10.1038/srep45064CrossRefGoogle ScholarPubMed
He, Q., Turel, O., Wei, L., & Bechara, A. (2020). Structural brain differences associated with extensive massively-multiplayer video gaming. Brain Imaging and Behavior, 15, 361374. https://doi.org/10.1007/s11682-020-00263-0Google Scholar
He, W., Qi, A., Wang, Q., et al. (2017). Abnormal reward and punishment sensitivity associated with internet addicts. Computers in Human Behavior, 75, 678683. https://doi.org/10.1016/j.chb.2017.06.017CrossRefGoogle Scholar
Hong, S. B., Kim, J. W., Choi, E. J., et al. (2013). Reduced orbitofrontal cortical thickness in male adolescents with internet addiction. Behavioral and Brain Functions, 9, Article 11. https://doi.org/10.1186/1744-9081-9-11CrossRefGoogle ScholarPubMed
Horowitz-Kraus, T., DiFrancesco, M., Greenwood, P., et al. (2020). Longer screen vs. reading time is related to greater functional connections between the salience network and executive functions regions in children with reading difficulties vs. typical readers. Child Psychiatry and Human Development, 52(4), 681692. https://doi.org/10.1007/s10578–020-01053-xCrossRefGoogle ScholarPubMed
Horowitz-Kraus, T., & Hutton, J. S. (2018). Brain connectivity in children is increased by the time they spend reading books and decreased by the length of exposure to screen-based media. Acta Paediatrica, International Journal of Paediatrics, 107(4), 685693. https://doi.org/10.1111/apa.14176CrossRefGoogle ScholarPubMed
Horvath, J., Mundinger, C., Schmitgen, M. M., et al. (2020). Structural and functional correlates of smartphone addiction. Addictive Behaviors, 105, Article 106334. https://doi.org/10.1016/j.addbeh.2020.106334CrossRefGoogle ScholarPubMed
Hutton, J. S., Dudley, J., Horowitz-Kraus, T., Dewitt, T., & Holland, S. K. (2020). Associations between screen-based media use and brain white matter integrity in preschool-aged children. JAMA Pediatrics, 174(1), e193869e193869. https://doi.org/10.1001/jamapediatrics.2019.3869CrossRefGoogle ScholarPubMed
Jiao, C., Wang, T., Peng, X., & Cui, F. (2017). Impaired empathy processing in individuals with internet addiction disorder: An event-related potential study. Frontiers in Human Neuroscience, 11, Article 498. https://doi.org/10.3389/fnhum.2017.00498CrossRefGoogle ScholarPubMed
Kanai, R., Bahrami, B., Roylance, R., & Rees, G. (2012). Online social network size is reflected in human brain structure. Proceedings of the Royal Society B: Biological Sciences, 279, 13271334. https://doi.org/10.1098/rspb.2011.1959CrossRefGoogle ScholarPubMed
Kei, K., Naoya, O., Sayaka, Y., et al. (2020). Relationship between media multitasking and functional connectivity in the dorsal attention network. Scientific Reports (Nature Publisher Group), 10(1), Article 17992. https://doi.org/10.1038/s41598-020-75091-9Google Scholar
Kilford, E. J., Garrett, E., & Blakemore, S.-J. (2016). The development of social cognition in adolescence: An integrated perspective. Neuroscience & Biobehavioral Reviews, 70, 106120. https://doi.org/10.1016/j.neubiorev.2016.08.016CrossRefGoogle ScholarPubMed
Kirby, B., Dapore, A., Ash, C., Malley, K., & West, R. (2020). Smartphone pathology, agency and reward processing. Lecture Notes in Information Systems and Organisation, 43, 321329. https://doi.org/10.1007/978-3-030-60073-0_37CrossRefGoogle Scholar
Ko, C.-H., Liu, G.-C., Hsiao, S., et al. (2009). Brain activities associated with gaming urge of online gaming addiction. Journal of Psychiatric Research, 43(7), 739747. https://doi.org/10.1016/j.jpsychires.2008.09.012CrossRefGoogle ScholarPubMed
Kuss, D. J., & Billieux, J. (2017). Technological addictions: Conceptualisation, measurement, etiology and treatment. Addictive Behaviors, 64, 231233. https://doi.org/10.1016/j.addbeh.2016.04.005CrossRefGoogle ScholarPubMed
Kuss, D. J., Pontes, H. M., & Griffiths, M. D. (2018). Neurobiological correlates in internet gaming disorder: A systematic literature review. Frontiers in Psychiatry, 9, Article 166. https://doi.org/10.3389/fpsyt.2018.00166CrossRefGoogle ScholarPubMed
Lauricella, A. R., Cingel, D. P., Beaudoin-Ryan, L., Robb, M. B., Saphir, M., & Wartella, E. A. (2016). The Common Sense census: Plugged-in parents of tweens and teens. Common Sense Media. https://www.commonsensemedia.org/sites/default/files/uploads/research/common-sense-parent-census_executivesummary_for-web.pdfGoogle Scholar
Lee, D., Namkoong, K., Lee, J., Lee, B. O., & Jung, Y. C. (2019). Lateral orbitofrontal gray matter abnormalities in subjects with problematic smartphone use. Journal of Behavioral Addictions, 8(3), 404411. https://doi.org/10.1556/2006.8.2019.50CrossRefGoogle ScholarPubMed
Li, W., Li, Y., Yang, W., et al. (2015). Brain structures and functional connectivity associated with individual differences in internet tendency in healthy young adults. Neuropsychologia, 70, 134144. https://doi.org/10.1016/j.neuropsychologia.2015.02.019CrossRefGoogle ScholarPubMed
Lieberman, M. D., & Eisenberger, N. I. (2009). Pains and pleasures of social life. Science, 323(5916), 890891. https://doi.org/10.1126/science.1170008CrossRefGoogle ScholarPubMed
Liebherr, M., Schubert, P., Antons, S., Montag, C., & Brand, M. (2020). Smartphones and attention, curse or blessing? A review on the effects of smartphone usage on attention, inhibition, and working memory. Computers in Human Behavior Reports, 1, Article 100005. https://doi.org/10.1016/j.chbr.2020.100005CrossRefGoogle Scholar
Lin, F., & Lei, H. (2015). Structural brain imaging and internet addiction. In Montag, C. & Reuter, M. (Eds.), Internet addiction (Studies in Neuroscience, Psychology and Behavioral Economics; pp. 2142). Springer. https://doi.org/10.1007/978-3-319-07242-5_2CrossRefGoogle ScholarPubMed
Lin, F., Zhou, Y., Du, Y., et al. (2012). Abnormal white matter integrity in adolescents with internet addiction disorder: A tract-based spatial statistics study. PLoS ONE, 7(1), e30253. https://doi.org/10.1371/journal.pone.0030253CrossRefGoogle ScholarPubMed
Lin, X., Dong, G., Wang, Q., & Du, X. (2015). Abnormal gray matter and white matter volume in ‘internet gaming addicts.Addictive Behaviors, 40, 137143. https://doi.org/10.1016/j.addbeh.2014.09.010CrossRefGoogle ScholarPubMed
Loh, K. K., Chakraborty, P., Sadhu, A., et al. (2019). Longitudinal cognitive and brain changes associated with one-month of increased internet access. Preprint. https://doi.org/10.31234/osf.io/p927zCrossRefGoogle Scholar
Loh, K. K., & Kanai, R. (2014). Higher media multi-tasking activity is associated with smaller gray-matter density in the anterior cingulate cortex. PLoS ONE, 9(9), Article e106698. https://doi.org/10.1371/journal.pone.0106698CrossRefGoogle ScholarPubMed
Lopez, R. B., Heatherton, T. F., & Wagner, D. D. (2020). Media multitasking is associated with higher risk for obesity and increased responsiveness to rewarding food stimuli. Brain Imaging and Behavior, 14(4), 10501061 https://doi.org/10.1007/s11682-019-00056-0CrossRefGoogle ScholarPubMed
Mars, R. B., Neubert, F.-X., Noonan, M. P., Sallet, J., Toni, I., & Rushworth, M. F. S. (2012). On the relationship between the “default mode network” and the “social brain.Frontiers in Human Neuroscience, 6, Article 189. https://doi.org/10.3389/fnhum.2012.00189CrossRefGoogle ScholarPubMed
Melchers, M., Li, M., Chen, Y., Zhang, W., & Montag, C. (2015). Low empathy is associated with problematic use of the internet: Empirical evidence from China and Germany. Asian Journal of Psychiatry, 17, 5660. https://doi.org/10.1016/j.ajp.2015.06.019CrossRefGoogle ScholarPubMed
Menon, V. (2015). Salience network. In Toga, A. W. (Ed.), Brain mapping: An encyclopedic reference (Vol. 2, pp. 597611). Academic Press. https://doi.org/10.1016/B978-0-12-397025-1.00052-XCrossRefGoogle Scholar
Meshi, D., Elizarova, A., Bender, A., & Verdejo-Garcia, A. (2019). Excessive social media users demonstrate impaired decision making in the Iowa Gambling Task. Journal of Behavioral Addictions, 8(1), 169173. https://doi.org/10.1556/2006.7.2018.138CrossRefGoogle ScholarPubMed
Meshi, D., Mamerow, L., Kirilina, E., Morawetz, C., Margulies, D. S., & Heekeren, H. R. (2016). Sharing self-related information is associated with intrinsic functional connectivity of cortical midline brain regions. Scientific Reports, 6(1), 111. https://doi.org/10.1038/srep22491CrossRefGoogle ScholarPubMed
Meshi, D., Morawetz, C., & Heekeren, H. R. (2013). Nucleus accumbens response to gains in reputation for the self relative to gains for others predicts social media use. Frontiers in Human Neuroscience, 7, Article 439. https://doi.org/10.3389/fnhum.2013.00439CrossRefGoogle ScholarPubMed
Meshi, D., Turel, O., & Henley, D. (2020). Snapchat vs. Facebook: Differences in problematic use, behavior change attempts, and trait social reward preferences. Addictive Behaviors Reports, 12, Article 100294. https://doi.org/10.1016/j.abrep.2020.100294CrossRefGoogle ScholarPubMed
Mills, K. L., Lalonde, F., Clasen, L. S., Giedd, J. N., & Blakemore, S.-J. (2014). Developmental changes in the structure of the social brain in late childhood and adolescence. Social Cognitive and Affective Neuroscience, 9(1), 123131. https://doi.org/10.1093/scan/nss113CrossRefGoogle ScholarPubMed
Mills, K. L., Siegmund, K. D., Tamnes, C. K., et al. (2021). Individual variability in structural brain development from late childhood to young adulthood. BioRxiv. https://doi.org/10.1016/j.neuroimage.2021.118450CrossRefGoogle Scholar
Minear, M., Brasher, F., McCurdy, M., Lewis, J., & Younggren, A. (2013). Working memory, fluid intelligence, and impulsiveness in heavy media multitaskers. Psychonomic Bulletin & Review, 20(6), 12741281. https://doi.org/10.3758/s13423–013-0456-6CrossRefGoogle ScholarPubMed
Moisala, M., Salmela, V., Hietajärvi, L., et al. (2016). Media multitasking is associated with distractibility and increased prefrontal activity in adolescents and young adults. NeuroImage, 134, 113121. https://doi.org/10.1016/j.neuroimage.2016.04.011CrossRefGoogle ScholarPubMed
Moisala, M., Salmela, V., Hietajärvi, L., et al. (2017). Gaming is related to enhanced working memory performance and task-related cortical activity. Brain Research, 1655, 204215. https://doi.org/10.1016/j.brainres.2016.10.027CrossRefGoogle ScholarPubMed
Montag, C., Markowetz, A., Blaszkiewicz, K., et al. (2017). Facebook usage on smartphones and gray matter volume of the nucleus accumbens. Behavioural Brain Research, 329, 221228. https://doi.org/10.1016/j.bbr.2017.04.035CrossRefGoogle ScholarPubMed
Ophir, E., Nass, C., & Wagner, A. D. (2009). Cognitive control in media multitaskers. Proceedings of the National Academy of Sciences, 106(37), 1558315587. https://doi.org/10.1073/pnas.0903620106CrossRefGoogle ScholarPubMed
Ophir, Y., Tikochinski, R., & Rosenberg, H. (2020). Science has not proven that screen use impacts children’s brain development. JAMA Pediatrics, 174(8), 805. https://doi.org/10.1001/jamapediatrics.2020.0635CrossRefGoogle Scholar
Parks, M. (2020). Social media usage is at an all-time high: That could mean a nightmare for democracy. National Public Radio. https://www.npr.org/2020/05/27/860369744/social-media-usage-is-at-an-all-time-high-that-could-mean-a-nightmare-for-democrGoogle Scholar
Paulus, M. P., Squeglia, L. M., Bagot, K., et al. (2019). Screen media activity and brain structure in youth: Evidence for diverse structural correlation networks from the ABCD study. NeuroImage, 185, 140153. https://doi.org/10.1016/j.neuroimage.2018.10.040CrossRefGoogle ScholarPubMed
Pinti, P., Tachtsidis, I., Hamilton, A., et al. (2020). The present and future use of functional near‐infrared spectroscopy (fNIRS) for cognitive neuroscience. Annals of the New York Academy of Sciences, 1464(1), 529. https://doi.org/10.1111/nyas.13948CrossRefGoogle ScholarPubMed
Przybylski, A. K., Orben, A., & Weinstein, N. (2020). How much is too much? Examining the relationship between digital screen engagement and psychosocial functioning in a confirmatory cohort study. Journal of the American Academy of Child & Adolescent Psychiatry, 59(9), 10801088. https://doi.org/10.1016/j.jaac.2019.06.017CrossRefGoogle Scholar
Rahimpour, A., Noubari, H. A., & Kazemian, M. (2018). A case-study of NIRS application for infant cerebral hemodynamic monitoring: A report of data analysis for feature extraction and infant classification into healthy and unhealthy. Informatics in Medicine Unlocked, 11, 4450. https://doi.org/10.1016/j.imu.2018.04.001CrossRefGoogle Scholar
Richardson, H., Lisandrelli, G., Riobueno-Naylor, A., & Saxe, R. (2018). Development of the social brain from age three to twelve years. Nature Communications, 9(1), 112. https://doi.org/10.1038/s41467-018-03399-2CrossRefGoogle ScholarPubMed
Sanbonmatsu, D. M., Strayer, D. L., Medeiros-Ward, N., & Watson, J. M. (2013). Who multi-tasks and why? Multi-tasking ability, perceived multi-tasking ability, impulsivity, and sensation seeking. PLoS ONE, 8(1), e54402. https://doi.org/10.1371/journal.pone.0054402CrossRefGoogle ScholarPubMed
Schmitgen, M. M., Horvath, J., Mundinger, C., et al. (2020). Neural correlates of cue reactivity in individuals with smartphone addiction. Addictive Behaviors, 108, Article 106422. https://doi.org/10.1016/j.addbeh.2020.106422CrossRefGoogle ScholarPubMed
Scholz, C., Baek, E. C., O’Donnell, M. B., Kim, H. S., Cappella, J. N., & Falk, E. B. (2017). A neural model of valuation and information virality. Proceedings of the National Academy of Sciences, 114(11), 28812886. https://doi.org/10.1073/pnas.1615259114CrossRefGoogle ScholarPubMed
Sherman, L. E., Greenfield, P. M., Hernandez, L. M., & Dapretto, M. (2018). Peer influence via Instagram: Effects on brain and behavior in adolescence and young adulthood. Child Development, 89(1), 3747. https://doi.org/10.1111/cdev.12838CrossRefGoogle ScholarPubMed
Sherman, L. E., Payton, A. A., Hernandez, L. M., Greenfield, P. M., & Dapretto, M. (2016). The power of the like in adolescence: Effects of peer influence on neural and behavioral responses to social media. Psychological Science, 19(1), 3964. https://doi.org/10.1177/0956797616645673Google Scholar
Shulman, E. P., Smith, A. R., Silva, K., et al. (2016). The dual systems model: Review, reappraisal, and reaffirmation. Developmental Cognitive Neuroscience, 17, 103117. https://doi.org/10.1016/j.dcn.2015.12.010CrossRefGoogle ScholarPubMed
Sisk, C. L., & Zehr, J. L. (2005). Pubertal hormones organize the adolescent brain and behavior. Frontiers in Neuroendocrinology, 26(3–4), 163174. https://doi.org/10.1016/j.yfrne.2005.10.003CrossRefGoogle ScholarPubMed
Smith, A. R., Chein, J., & Steinberg, L. (2013). Impact of socio-emotional context, brain development, and pubertal maturation on adolescent risk-taking. Hormones and Behavior, 64(2), 323332. https://doi.org/10.1016/j.yhbeh.2013.03.006CrossRefGoogle ScholarPubMed
Spear, L. (2010). The behavioral neuroscience of adolescence. W.W. Norton & Company.Google ScholarPubMed
Spies Shapiro, L. A., & Margolin, G. (2014). Growing up wired: Social networking sites and adolescent psychosocial development. Clinical Child and Family Psychology Review, 17(1), 118. https://doi.org/10.1007/s10567–013-0135-1CrossRefGoogle ScholarPubMed
Steinberg, L. (2008). A social neuroscience perspective on adolescent risk-taking. Developmental Review, 28(1), 78106. https://doi.org/10.1016/j.dr.2007.08.002CrossRefGoogle ScholarPubMed
Stramaccia, D. F., Penolazzi, B., Sartori, G., Braga, M., Mondini, S., & Galfano, G. (2015). Assessing the effects of tDCS over a delayed response inhibition task by targeting the right inferior frontal gyrus and right dorsolateral prefrontal cortex. Experimental Brain Research, 233(8), 22832290. https://doi.org/10.1007/s00221-015-4297-6CrossRefGoogle Scholar
Sun, Y., Ying, H., Seetohul, R. M., et al. (2012). Brain fMRI study of crave induced by cue pictures in online game addicts (male adolescents). Behavioural Brain Research, 233(2), 563576. https://doi.org/10.1016/j.bbr.2012.05.005CrossRefGoogle ScholarPubMed
Sutter, M., Zoller, C., & Glätzle-Rützler, D. (2019). Economic behavior of children and adolescents: A first survey of experimental economics results. European Economic Review, 111, 98121. https://doi.org/10.1016/j.euroecorev.2018.09.004CrossRefGoogle Scholar
Takeuchi, H., Taki, Y., Asano, K., et al. (2018). Impact of frequency of internet use on development of brain structures and verbal intelligence: Longitudinal analyses. Human Brain Mapping, 39(11), 44714479. https://doi.org/10.1002/hbm.24286CrossRefGoogle ScholarPubMed
Takeuchi, H., Taki, Y., Hashizume, H., et al. (2015). The impact of television viewing on brain structures: Cross-sectional and longitudinal analyses. Cerebral Cortex, 25(5), 11881197. https://doi.org/10.1093/cercor/bht315CrossRefGoogle ScholarPubMed
Takeuchi, H., Taki, Y., Hashizume, H., et al. (2016). Impact of videogame play on the brain’s microstructural properties: Cross-sectional and longitudinal analyses. Molecular Psychiatry, 21(12), 17811789. https://doi.org/10.1038/mp.2015.193CrossRefGoogle ScholarPubMed
Tang, Z., Zhang, H., Yan, A., & Qu, C. (2017). Time is money: The decision making of smartphone high users in gain and loss intertemporal choice. Frontiers in Psychology, 8, Article 363. https://doi.org/10.3389/fpsyg.2017.00363CrossRefGoogle ScholarPubMed
Turel, O., He, Q., Brevers, D., & Bechara, A. (2018). Social networking sites use and the morphology of a social-semantic brain network. Social Neuroscience, 13(5), 628636. https://doi.org/10.1080/17470919.2017.1382387CrossRefGoogle ScholarPubMed
Turel, O., He, Q., Wei, L., & Bechara, A. (2020). The role of the insula in internet gaming disorder. Addiction Biology, 26(2), e12894. https://doi.org/10.1111/adb.12894CrossRefGoogle ScholarPubMed
Turel, O., He, Q., Xue, G., Xiao, L., & Bechara, A. (2014). Examination of neural systems sub-serving Facebook “addiction.Psychological Reports, 115(3), 675695. https://doi.org/10.2466/18.PR0.115c31z8CrossRefGoogle ScholarPubMed
Twenge, J. M., Haidt, J., Joiner, T. E., & Campbell, W. K. (2020). Underestimating digital media harm. Nature Human Behaviour, 4(4), 346348. https://doi.org/10.1038/s41562–020-0839-4CrossRefGoogle ScholarPubMed
Tymofiyeva, O., Yuan, J. P., Kidambi, R., et al. (2020). Neural correlates of smartphone dependence in adolescents. Frontiers in Human Neuroscience, 14, Article 428. https://doi.org/10.3389/fnhum.2020.564629CrossRefGoogle ScholarPubMed
Volkow, N. D., Koob, G. F., Croyle, R. T., et al. (2018). The conception of the ABCD study: From substance use to a broad NIH collaboration. Developmental Cognitive Neuroscience, 32, 47. https://doi.org/10.1016/j.dcn.2017.10.002CrossRefGoogle ScholarPubMed
Von der Heide, R., Vyas, G., & Olson, I. R. (2013). The social network-network: Size is predicted by brain structure and function in the amygdala and paralimbic regions. Social Cognitive and Affective Neuroscience, 9(12), 19621972. https://doi.org/10.1093/scan/nsu009CrossRefGoogle Scholar
Wang, H., Jin, C., Yuan, K., et al. (2015). The alteration of gray matter volume and cognitive control in adolescents with internet gaming disorder. Frontiers in Behavioral Neuroscience, 9, Article 64. https://doi.org/10.3389/fnbeh.2015.00064CrossRefGoogle ScholarPubMed
Wang, T., Ge, Y., Zhang, J., Liu, J., & Luo, W. (2014). The capacity for pain empathy among urban internet-addicted left-behind children in China: An event-related potential study. Computers in Human Behavior, 33, 5662. https://doi.org/10.1016/j.chb.2013.12.020CrossRefGoogle Scholar
Wang, Y., Zou, Z., Song, H., & Xu, X. (2016). Altered gray matter volume and white matter integrity in college students with mobile phone dependence. Frontiers in Psychology, 7, Article 597. https://doi.org/10.3389/fpsyg.2016.00597Google ScholarPubMed
Ward, A. F., Duke, K., Gneezy, A., & Bos, M. W. (2017). Brain drain: The mere presence of one’s own smartphone reduces available cognitive capacity. Journal of the Association for Consumer Research, 2(2), 140154. https://doi.org/10.1086/691462CrossRefGoogle Scholar
Wei, L., Zhang, S., Turel, O., Bechara, A., & He, Q. (2017). A tripartite neurocognitive model of internet gaming disorder. Frontiers in Psychiatry, 8, Article 285. https://doi.org/10.3389/fpsyt.2017.00285CrossRefGoogle ScholarPubMed
Wilmer, H. H., & Chein, J. M. (2016). Mobile technology habits: Patterns of association among device usage, intertemporal preference, impulse control, and reward sensitivity. Psychonomic Bulletin and Review, 23(5), 16071614. https://doi.org/10.3758/s13423–016-1011-zCrossRefGoogle ScholarPubMed
Wilmer, H. H., Hampton, W. H., Olino, T. M., Olson, I. R., & Chein, J. M. (2019). Wired to be connected? Links between mobile technology engagement, intertemporal preference and frontostriatal white matter connectivity. Social Cognitive and Affective Neuroscience, 14(4), 367379. https://doi.org/10.1093/scan/nsz024Google ScholarPubMed
Wilmer, H. H., Sherman, L. E., & Chein, J. M. (2017). Smartphones and cognition: A review of research exploring the links between mobile technology habits and cognitive functioning. Frontiers in Psychology, 8, Article 605. https://doi.org/10.3389/fpsyg.2017.00605CrossRefGoogle ScholarPubMed
Yao, Y. W., Liu, L., Ma, S. S., et al. (2017). Functional and structural neural alterations in internet gaming disorder: A systematic review and meta-analysis. Neuroscience and Biobehavioral Reviews, 83, 313324. https://doi.org/10.1016/j.neubiorev.2017.10.029CrossRefGoogle ScholarPubMed
Yuan, K., Qin, W., Wang, G., et al. (2011). Microstructure abnormalities in adolescents with internet addiction disorder. PLoS ONE, 6(6), e20708. https://doi.org/10.1371/journal.pone.0020708CrossRefGoogle ScholarPubMed
Zhang, Y., Mei, S., Li, L., Chai, J., Li, J., & Du, H. (2015). The relationship between impulsivity and internet addiction in Chinese college students: A moderated mediation analysis of meaning in life and self-esteem. PLoS ONE, 10(7), e0131597. https://doi.org/10.1371/journal.pone.0131597CrossRefGoogle Scholar
Zhou, F., Montag, C., Sariyska, R., et al. (2019). Orbitofrontal gray matter deficits as marker of internet gaming disorder: Converging evidence from a cross‐sectional and prospective longitudinal design. Addiction Biology, 24(1), 100109. https://doi.org/10.1111/adb.12570CrossRefGoogle ScholarPubMed
Zivan, M., Bar, S., Jing, X., Hutton, J., Farah, R., & Horowitz-Kraus, T. (2019). Screen-exposure and altered brain activation related to attention in preschool children: An EEG study. Trends in Neuroscience and Education, 1(1), 3242. https://doi.org/10.1016/j.tine.2019.100117Google Scholar

References

Baams, L., Overbeek, G., Dubas, J. S., Doornwaard, S. M., Rommes, E., & Van Aken, M. A. (2015). Perceived realism moderates the relation between sexualized media consumption and permissive sexual attitudes in Dutch adolescents. Archives of Sexual Behavior, 44(3), 743754.CrossRefGoogle ScholarPubMed
Bandura, A. (2001). Social cognitive theory of mass communication. Media Psychology, 3(3), 265299.CrossRefGoogle Scholar
Beals, L. M. (2010). Content creation in virtual worlds to support adolescent identity development. New Directions for Youth Development, 2010(128), 4553.CrossRefGoogle ScholarPubMed
Bem, D. J. (1972). Self-perception theory. In Berkowitz, L. (Ed.), Advances in experimental social psychology (Vol. 6, p. 162). Elsevier.Google Scholar
Blais, J. J., Craig, W. M., Pepler, D., & Connolly, J. (2008). Adolescents online: The importance of internet activity choices to salient relationships. Journal of Youth and Adolescence, 37(5), 522536.CrossRefGoogle Scholar
Blumer, M., & Hertlein, K. M. (2015). The technological genogram: A tool for exploring intergenerational communication patterns around technology use. In Breuss, C. (Ed.), Family communication in the digital age (pp. 471490). Peter Lang International Publishers.Google Scholar
Bobkowski, P. S., Shafer, A., & Ortiz, R. R. (2016). Sexual intensity of adolescents' online self-presentations: Joint contribution of identity, media consumption, and extraversion. Computers in Human Behavior, 58, 6474.CrossRefGoogle Scholar
Borrajo, E., Gámez-Guadix, M., & Calvete, E. (2015). Cyber dating abuse: Prevalence, context, and relationship with offline dating aggression. Psychological Reports, 116(2), 565585.CrossRefGoogle ScholarPubMed
Brown, J. D., & L’Engle, K. L. (2009). X-rated: Sexual attitudes and behaviors associated with US early adolescents’ exposure to sexually explicit media. Communication Research, 36(1), 129151.CrossRefGoogle Scholar
Burén, J., & Lunde, C. (2018). Sexting among adolescents: A nuanced and gendered online challenge for young people. Computers in Human Behavior, 85, 210217.CrossRefGoogle Scholar
Burkett, M. (2015). Sex(t) talk: A qualitative analysis of young adults’ negotiations of the pleasures and perils of sexting. Sexuality & Culture: An Interdisciplinary Quarterly, 19(4), 835863.CrossRefGoogle Scholar
Burt, M. R. (1980). Cultural myths and supports for rape. Journal of Personality and Social Psychology, 38(2), 217230.CrossRefGoogle ScholarPubMed
Carrotte, E. R., Davis, A. C., & Lim, M. S. (2020). Sexual behaviors and violence in pornography: Systematic review and narrative synthesis of video content analyses. Journal of Medical Internet Research, 22(5), e16702.CrossRefGoogle ScholarPubMed
Chen, A. S., Leung, M., Chen, C. H., & Yang, S. C. (2013). Exposure to internet pornography among Taiwanese adolescents. Social Behavior and Personality: An International Journal, 41(1), 157164.CrossRefGoogle Scholar
Chen, K. J., & Cheung, H. L. (2019). Unlocking the power of ephemeral content: The roles of motivations, gratification, need for closure, and engagement. Computers in Human Behavior, 97, 6774.CrossRefGoogle Scholar
Cheng, S., Hamilton, L., Missari, S., & Ma, J. (2014). Sexual subjectivity among adolescent girls: Social disadvantage and young adult outcomes. Social Forces, 93(2), 515544.CrossRefGoogle Scholar
Choi, H., Van Ouytsel, J., & Temple, J. R. (2016). Association between sexting and sexual coercion among female adolescents. Journal of Adolescence, 53, 164168.CrossRefGoogle ScholarPubMed
Christie, D., & Viner, R. (2005). Adolescent development. BMJ, 330(7486), 301304.CrossRefGoogle ScholarPubMed
Collins, R. L., Martino, S., & Shaw, R. (2010). Influence of new media on adolescent sexual health (Working Paper WR-761). Rand Health.Google Scholar
Cooper, A., Scherer, C. R., Boies, S. C., & Gordon, B. L. (1999). Sexuality on the internet: From sexual exploration to pathological expression. Professional Psychology: Research and Practice, 30(2), 154164.CrossRefGoogle Scholar
Cooper, K., Quayle, E., Jonsson, L., & Svedin, C. G. (2016). Adolescents and self-taken sexual images: A review of the literature. Computers in Human Behavior, 55, 706716.CrossRefGoogle Scholar
Crofts, T., Lee, M., McGovern, A., & Milivojevic, S. (2018). Sexting pleasures: Young people, fun, flirtation, and child pornography. In Grealy, L., Driscoll, C., & Hickey-Moody, A. (Eds.), Youth, technology, governance, experience: Adults understanding young people (pp. 103122). Routledge.CrossRefGoogle Scholar
Dake, J. A., Price, J. H., Maziarz, L., & Ward, B. (2012). Prevalence and correlates of sexting behavior in adolescents. American Journal of Sexuality Education, 7(1), 115.CrossRefGoogle Scholar
Davis, K. (2013). Young people’s digital lives: The impact of interpersonal relationships and digital media use on adolescents’ sense of identity. Computers in Human Behavior, 29(6), 22812293.CrossRefGoogle Scholar
de Leeuw, R. N., & Buijzen, M. (2016). Introducing positive media psychology to the field of children, adolescents, and media. Journal of Children and Media, 10(1), 3946.CrossRefGoogle Scholar
de Lenne, O., Vandenbosch, L., Eggermont, S., Karsay, K., & Trekels, J. (2018). Picture-perfect lives on social media: A cross-national study on the role of media ideals in adolescent well-being. Media Psychology, 23(1), 127.Google Scholar
de Vaate, A. J. N. B., Veldhuis, J., Alleva, J. M., Konijn, E. A., & van Hugten, C. H. (2018). Show your best self(ie): An exploratory study on selfie-related motivations and behavior in emerging adulthood. Telematics and Informatics, 35(5), 13921407.CrossRefGoogle Scholar
DeLamater, J., & Friedrich, W. N. (2002). Human sexual development. Journal of Sex Research, 39(1), 1014.CrossRefGoogle ScholarPubMed
Diamond, L. M., & Savin-Williams, R. C. (2009). Adolescent sexuality. In Lerner, M. & Steinberg, L. (Eds.), Handbook of adolescent psychology (pp. 479523). John Wiley & Sons, Inc.Google Scholar
Dir, A. L., Coskunpinar, A., Steiner, J. L., & Cyders, M. A. (2013). Understanding differences in sexting behaviors across gender, relationship status, and sexual identity, and the role of expectancies in sexting. Cyberpsychology, Behavior, and Social Networking, 16(8), 568574.CrossRefGoogle ScholarPubMed
Donevan, M., & Mattebo, M. (2017). The relationship between frequent pornography consumption, behaviours, and sexual preoccupancy among male adolescents in Sweden. Sexual & Reproductive Healthcare: Official Journal of the Swedish Association of Midwives, 12, 8287.CrossRefGoogle ScholarPubMed
Doornwaard, S. M., Bickham, D. S., Rich, M., ter Bogt, T. F., & van den Eijnden, R. J. (2015). Adolescents’ use of sexually explicit internet material and their sexual attitudes and behavior: Parallel development and directional effects. Developmental Psychology, 51(10), 14761488.CrossRefGoogle ScholarPubMed
Doornwaard, S. M., Bickham, D. S., Rich, M., Vanwesenbeeck, I., van den Eijnden, R. J., & ter Bogt, T. F. (2014). Sex-related online behaviors and adolescents’ body and sexual self-perceptions. Pediatrics, 134(6), 11031110.CrossRefGoogle ScholarPubMed
Draucker, C. B., & Martsolf, D. S. (2010). The role of electronic communication technology in adolescent dating violence. Journal of Child and Adolescent Psychiatric Nursing, 23(3), 133142.CrossRefGoogle ScholarPubMed
Fortenberry, J. D. (2013). Sexual development in adolescents. In Bromberg, D. S. & O’Donohue, W. T. (Eds.), Handbook of child and adolescent sexuality: Developmental and forensic psychology (p. 171192). Elsevier Academic Press.CrossRefGoogle Scholar
Fredrickson, B. L., & Roberts, T. A. (1997). Objectification theory: Toward understanding women’s lived experiences and mental health risks. Psychology of Women Quarterly, 21(2), 173206.CrossRefGoogle Scholar
Gagnon, J. H., & Simon, W. (1973). Sexual conduct. Aldine.Google Scholar
Galovan, A. M., Drouin, M., & McDaniel, B. T. (2018). Sexting profiles in the United States and Canada: Implications for individual and relationship well-being. Computers in Human Behavior, 79, 1929.CrossRefGoogle Scholar
Grubbs, J. B., Wright, P. J., Braden, A. L., Wilt, J. A., & Kraus, S. W. (2019). Internet pornography use and sexual motivation: A systematic review and integration. Annals of the International Communication Association, 43(2), 117155.CrossRefGoogle Scholar
Guse, K., Levine, D., Martins, S., et al. (2012). Interventions using new digital media to improve adolescent sexual health: A systematic review. Journal of Adolescent Health, 51(6), 535543.CrossRefGoogle ScholarPubMed
Hamilton, L., & Armstrong, E. A. (2009). Gendered sexuality in young adulthood: Double binds and flawed options. Gender & Society, 23(5), 589616.CrossRefGoogle Scholar
Hardy, S. A., Hurst, J. L., Price, J., & Denton, M. L. (2019). The socialization of attitudes about sex and their role in adolescent pornography use. Journal of Adolescence, 72, 7082.CrossRefGoogle ScholarPubMed
Hellevik, P. M. (2019). Teenagers' personal accounts of experiences with digital intimate partner violence and abuse. Computers in Human Behavior, 92, 178187.CrossRefGoogle Scholar
Kapidzic, S., & Herring, S. C. (2015). Race, gender, and self-presentation in teen profile photographs. New Media & Society, 17(6), 958976.CrossRefGoogle Scholar
Katz, E., Blumler, J. G., & Gurevitch, M. (1973). Uses and gratifications research. The Public Opinion Quarterly, 37(4), 509523.CrossRefGoogle Scholar
Kelly, A. E., & Rodriguez, R. R. (2006). Publicly committing oneself to an identity. Basic and Applied Social Psychology, 28(2), 185191.CrossRefGoogle Scholar
Klaassen, M. J., & Peter, J. (2015). Gender (in) equality in internet pornography: A content analysis of popular pornographic internet videos. The Journal of Sex Research, 52(7), 721735.CrossRefGoogle ScholarPubMed
Klein, V., Šević, S., Kohut, T., & Štulhofer, A. (2020). Longitudinal assessment of the association between the use of sexually explicit material, hyperfemininity, and sexual agency in adolescent women. Psychology & Sexuality, 1–15.Google Scholar
Korchmaros, J. D., Ybarra, M. L., & Mitchell, K. J. (2015). Adolescent online romantic relationship initiation: Differences by sexual and gender identification. Journal of Adolescence, 40, 5464.CrossRefGoogle ScholarPubMed
Le, V. D., Temple, J. R., Peskin, M., Markham, C., & Tortolero, S. (2014). Sexual behavior and communication. In Weins, W. J. & Hiestand, T. C. (Eds.), Sexting and youth: A multidisciplinary examination of research, theory, and law (pp. 6394). Carolina Academic Press.Google Scholar
Lerner, R. M., Boyd, M. J., & Du, D. (2010). Adolescent development. In Weiner, I. B. & Craighead, W. E. (Eds.), The Corsini encyclopedia of psychology (4th ed.; pp. 3536). Wiley.Google Scholar
Ling, R., & Bertel, T. (2013). Mobile communication culture among children and adolescents. In Lemish, D. (Ed.), The Routledge international handbook of children, adolescents and media (pp. 153159). Routledge.Google Scholar
Lippman, J. R., & Campbell, S. W. (2014). Damned if you do, damned if you don’t… if you’re a girl: Relational and normative contexts of adolescent sexting in the United States. Journal of Children and Media, 8(4), 371386.CrossRefGoogle Scholar
Luder, M. T., Pittet, I., Berchtold, A., Akré, C., Michaud, P. A., & Surís, J. C. (2011). Associations between online pornography and sexual behavior among adolescents: Myth or reality?. Archives of Sexual Behavior, 40(5), 10271035.CrossRefGoogle ScholarPubMed
MacDonald, K., Imburgia, T. M., Auerswald, C., & Ott, M. A. (2018). Sexting among adolescent urban males. Journal of Adolescent Health, 62(2), S126.CrossRefGoogle Scholar
Machimbarrena, J. M., Calvete, E., Fernández-González, L., Álvarez-Bardón, A., Álvarez-Fernández, L., & González-Cabrera, J. (2018). Internet risks: An overview of victimization in cyberbullying, cyber dating abuse, sexting, online grooming and problematic internet use. International journal of Environmental Research and Public Health, 15(11), 2471.CrossRefGoogle ScholarPubMed
Madigan, S., Ly, A., Rash, C. L., Van Ouytsel, J., & Temple, J. R. (2018). Prevalence of multiple forms of sexting behavior among youth: A systematic review and meta-analysis. JAMA Pediatrics, 172(4), 327335.CrossRefGoogle ScholarPubMed
Maes, C., Schreurs, L., van Oosten, J. M., & Vandenbosch, L. (2019). #(Me) too much? The role of sexualizing online media in adolescents’ resistance towards the metoo-movement and acceptance of rape myths. Journal of Adolescence, 77, 5969.CrossRefGoogle ScholarPubMed
Maes, C., Trekels, J., Impett, E., & Vandenbosch, L. (2022). The Development of the Positive Sexuality in Adolescence Scale (PSAS). The Journal of Sex Research, 1–17. https://doi.org/10.1080/00224499.2021.2011826CrossRefGoogle Scholar
Maheux, A. J., Evans, R., Widman, L., Nesi, J., Prinstein, M. J., & Choukas-Bradley, S. (2020). Popular peer norms and adolescent sexting behavior. Journal of Adolescence, 78, 6266.CrossRefGoogle ScholarPubMed
Maloney, M., Roberts, S., & Caruso, A. (2018). ‘Mmm … I love it, bro!’: Performances of masculinity in YouTube gaming. New Media and Society, 20, 16971714.CrossRefGoogle Scholar
Manago, A. M., Ward, L. M., Lemm, K. M., Reed, L., & Seabrook, R. (2015). Facebook involvement, objectified body consciousness, body shame, and sexual assertiveness in college women and men. Sex Roles, 72(1–2), 114.CrossRefGoogle Scholar
Marengo, D., Settanni, M., & Longobardi, C. (2019). The associations between sex drive, sexual self-concept, sexual orientation, and exposure to online victimization in Italian adolescents: Investigating the mediating role of verbal and visual sexting behaviors. Children and Youth Services Review, 102, 1826.CrossRefGoogle Scholar
Mascheroni, G., Vincent, J., & Jimenez, E. (2015). “Girls are addicted to likes so they post semi-naked selfies”: Peer mediation, normativity and the construction of identity online. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 9(1).CrossRefGoogle Scholar
Milas, G., Klarić, I. M., Malnar, A., Šupe‐Domić, D., & Slavich, G. M. (2019). Socioeconomic status, social‐cultural values, life stress, and health behaviors in a national sample of adolescents. Stress and Health, 35(2), 217224.CrossRefGoogle Scholar
Molyneaux, H., O’Donnell, S., Gibson, K., & Singer, J. (2008). Exploring the gender divide on YouTube: An analysis of the creation and reception of vlogs. American Communication Journal, 10, 113.Google Scholar
Moran, J. (2000) Teaching sex: The shaping of adolescence in the 20th century. Harvard University Press.Google Scholar
Morey, J. N., Gentzler, A. L., Creasy, B., Oberhauser, A. M., & Westerman, D. (2013). Young adults’ use of communication technology within their romantic relationships and associations with attachment style. Computers in Human Behavior, 29(4), 17711778.CrossRefGoogle Scholar
Mori, C., Temple, J. R., Browne, D., & Madigan, S. (2019). Association of sexting with sexual behaviors and mental health among adolescents: A systematic review and meta-analysis. JAMA Pediatrics, 173(8), 770779.CrossRefGoogle ScholarPubMed
Morris, M., & Anderson, E. (2015). ‘Charlie is so cool like’: Authenticity, popularity and inclusive masculinity on YouTube. Sociology, 49(6), 12001217.CrossRefGoogle Scholar
Murray, S. H. (2018). Heterosexual men’s sexual desire: Supported by, or deviating from, traditional masculinity norms and sexual scripts?. Sex Roles, 78(1–2), 130141.CrossRefGoogle Scholar
Nikkelen, S. W. C., van Oosten, J. M. F., & van den Borne, M. M. J. J. (2020). Sexuality education in the digital era: Intrinsic and extrinsic predictors of online sexual information seeking among youth. Journal of Sex Research, 57(2), 189199.CrossRefGoogle ScholarPubMed
Park, E., & Kwon, M. (2018). Health-related internet use by children and adolescents: Systematic review. Journal of Medical Internet Research, 20(4), e7731.CrossRefGoogle ScholarPubMed
Parker, T. S., Blackburn, K. M., Perry, M. S., & Hawks, J. M. (2013). Sexting as an intervention: Relationship satisfaction and motivation considerations. The American Journal of Family Therapy, 41(1), 112.CrossRefGoogle Scholar
Pascoe, C. J. (2011). Resource and risk: Youth sexuality and new media use. Sexuality Research and Social Policy, 8(1), 517.CrossRefGoogle Scholar
Perry, D. G., & Pauletti, R. E. (2011). Gender and adolescent development. Journal of Research on Adolescence, 21(1), 6174.CrossRefGoogle Scholar
Peter, J., & Valkenburg, P. M. (2006). Adolescents’ exposure to sexually explicit online material and recreational attitudes toward sex. Journal of Communication, 56(4), 639660.CrossRefGoogle Scholar
Peter, J., & Valkenburg, P. M. (2008). Adolescents’ exposure to sexually explicit internet material, sexual uncertainty, and attitudes toward uncommitted sexual exploration: Is there a link?. Communication Research, 35(5), 579601.CrossRefGoogle Scholar
Peter, J., & Valkenburg, P. M. (2009). Adolescents’ exposure to sexually explicit internet material and notions of women as sex objects: Assessing causality and underlying processes. Journal of Communication, 59(3), 407433.CrossRefGoogle Scholar
Peter, J., & Valkenburg, P. M. (2010). Adolescents’ use of sexually explicit internet material and sexual uncertainty: The role of involvement and gender. Communication Monographs, 77(3), 357375.CrossRefGoogle Scholar
Peter, J., & Valkenburg, P. M. (2011). The use of sexually explicit internet material and its antecedents: A longitudinal comparison of adolescents and adults. Archives of Sexual Behavior, 40(5), 10151025.CrossRefGoogle ScholarPubMed
Peter, J., & Valkenburg, P. M. (2016). Adolescents and pornography: A review of 20 years of research. The Journal of Sex Research, 53(4–5), 509531.CrossRefGoogle ScholarPubMed
Petersen, J. L., & Hyde, J. S. (2010). A meta-analytic review of research on gender differences in sexuality, 1993–2007. Psychological Bulletin, 136(1), 2138.CrossRefGoogle ScholarPubMed
Ponton, L. E., & Judice, S. (2004). Typical adolescent sexual development. Child and Adolescent Psychiatric Clinics of North America, 13(3), 497511.CrossRefGoogle ScholarPubMed
Popa, D., & Gavriliu, D. (2015). Gender representations and digital media. Procedia – Social and Behavioral Sciences, 180, 11991206.CrossRefGoogle Scholar
Reed, L. A., Tolman, R. M., & Ward, L. M. (2017). Gender matters: Experiences and consequences of digital dating abuse victimization in adolescent dating relationships. Journal of Adolescence, 59, 7989.CrossRefGoogle ScholarPubMed
Reed, L. A., Ward, L. M., Tolman, R. M., Lippman, J. R., & Seabrook, R. C. (2018). The association between stereotypical gender and dating beliefs and digital dating abuse perpetration in adolescent dating relationships. Journal of Interpersonal Violence, 36(9–10), NP5561NP5585.CrossRefGoogle ScholarPubMed
Reitz, E., van de Bongardt, D., Baams, L., et al. (2015). Project STARS (Studies on Trajectories of Adolescent Relationships and Sexuality): A longitudinal, multi-domain study on sexual development of Dutch adolescents. European Journal of Developmental Psychology, 12(5), 613626.CrossRefGoogle Scholar
Rice, E., Craddock, J., Hemler, M., et al. (2018). Associations between sexting behaviors and sexual behaviors among mobile phone‐owning teens in Los Angeles. Child Development, 89(1), 110117.CrossRefGoogle ScholarPubMed
Ringrose, J. (2011). Are you sexy, flirty, or a slut? Exploring ‘sexualization’and how teen girls perform/negotiate digital sexual identity on social networking sites. In Gill, R. & Scharf, C. (Eds.), New femininities (pp. 99116). Palgrave Macmillan.CrossRefGoogle Scholar
Ringrose, J., & Harvey, L. (2015). Boobs, back-off, six packs and bits: Mediated body parts, gendered reward, and sexual shame in teens’ sexting images. Continuum, 29(2), 205217.CrossRefGoogle Scholar
Ringrose, J., Harvey, L., Gill, R., & Livingstone, S. (2013). Teen girls, sexual double standards and “sexting”: Gendered value in digital image exchange. Feminist Theory, 14(3), 305323.CrossRefGoogle Scholar
Romo, D. L., Garnett, C., Younger, A. P., et al. (2017). Social media use and its association with sexual risk and parental monitoring among a primarily Hispanic adolescent population. Journal of Pediatric and Adolescent Gynecology, 30(4), 466473.CrossRefGoogle ScholarPubMed
Rueda, H. A., Lindsay, M., & Williams, L. R. (2015). “She posted it on Facebook”: Mexican American adolescents’ experiences with technology and romantic relationship conflict. Journal of Adolescent Research, 30(4), 419445.CrossRefGoogle Scholar
Russell, S. T. (2005). Conceptualizing positive adolescent sexuality development. Sexuality Research and Social Policy, 2(3), 412.CrossRefGoogle Scholar
Saewyc, E. M. (2011). Research on adolescent sexual orientation: Development, health disparities, stigma, and resilience. Journal of Research on Adolescence, 21(1), 256272.CrossRefGoogle ScholarPubMed
Sawyer, S. M., Afifi, R. A., Bearinger, L. H., et al. (2012). Adolescence: A foundation for future health. The Lancet, 379(9826), 16301640.CrossRefGoogle ScholarPubMed
Scarcelli, C. M. (2015). ‘It is disgusting, but…’: Adolescent girls’ relationship to internet pornography as gender performance. Porn Studies, 2(2–3), 237249.CrossRefGoogle Scholar
Schlenker, B. R., Dlugolecki, D. W., & Doherty, K. (1994). The impact of self-presentations on self-appraisals and behavior: The power of public commitment. Personality and Social Psychology Bulletin, 20(1), 2033.CrossRefGoogle Scholar
Schlenker, B. R., Wowra, S. A., Johnson, R. M., & Miller, M. L. (2008). The impact of imagined audiences on self-appraisals. Personal Relationships, 15(2), 247260.CrossRefGoogle Scholar
Seligman, M. E., & Csikszentmihalyi, M. (2014). Positive psychology: An introduction. In Csikszentmihalyi, M., Flow and the foundations of positive psychology (pp. 279298). Springer.CrossRefGoogle Scholar
Shafer, A., Bobkowski, P., & Brown, J. D. (2013). Sexual media practice: How adolescents select, engage with, and are affected by sexual media. In Dill, K. E. (Ed.), The Oxford handbook of media psychology (pp. 223251). Oxford University Press.CrossRefGoogle Scholar
Shek, D. T., & Ma, C. M. (2016). A six-year longitudinal study of consumption of pornographic materials in Chinese adolescents in Hong Kong. Journal of Pediatric and Adolescent Gynecology, 29(1), 1221.CrossRefGoogle ScholarPubMed
Simon, L., & Daneback, K. (2013). Adolescents’ use of the internet for sex education: A thematic and critical review of the literature. International Journal of Sexual Health, 25(4), 305319.CrossRefGoogle Scholar
Sundar, S. S., & Limperos, A. M. (2013). Uses and grats 2.0: New gratifications for new media. Journal of Broadcasting & Electronic Media, 57(4), 504525.CrossRefGoogle Scholar
To, S., Ngai, S. S., & Iu Kan, S. (2012). Direct and mediating effects of accessing sexually explicit online materials on Hong Kong adolescents’ attitude, knowledge, and behavior relating to sex. Children and Youth Services Review, 34(11), 21562163.CrossRefGoogle Scholar
Tolman, D. L., & McClelland, S. I. (2011). Normative sexuality development in adolescence: A decade in review, 2000–2009. Journal of Research on Adolescence, 21(1), 242255.CrossRefGoogle Scholar
Trottier, D., Benbouriche, M., & Bonneville, V. (2021). A meta-analysis on the association between rape myth acceptance and sexual coercion perpetration. The Journal of Sex Research, 58(3), 375382.CrossRefGoogle ScholarPubMed
Utz, S., & Beukeboom, C. J. (2011). The role of social network sites in romantic relationships: Effects on jealousy and relationship happiness. Journal of Computer-Mediated Communication, 16(4), 511527.CrossRefGoogle Scholar
Valkenburg, P. M. (2017). Understanding self-effects in social media. Human Communication Research, 43(4), 477490.CrossRefGoogle Scholar
Valkenburg, P. M., & Peter, J. (2011). Online communication among adolescents: An integrated model of its attraction, opportunities, and risks. Journal of Adolescent Health, 48(2), 121127.CrossRefGoogle ScholarPubMed
Van de Bongardt, D., Yu, R., Deković, M., & Meeus, W. H. (2015). Romantic relationships and sexuality in adolescence and young adulthood: The role of parents, peers, and partners. European Journal of Developmental Psychology, 12(5), 497515.CrossRefGoogle Scholar
Van Oosten, J. M., de Vries, D. A., & Peter, J. (2018). The importance of adolescents’ sexually outgoing self-concept: Differential roles of self-and other-generated sexy self-presentations in social media. Cyberpsychology, Behavior, and Social Networking, 21(1), 510.CrossRefGoogle ScholarPubMed
Van Oosten, J. M., Peter, J., & Boot, I. (2015). Exploring associations between exposure to sexy online self-presentations and adolescents’ sexual attitudes and behavior. Journal of Youth and Adolescence, 44(5), 10781091.CrossRefGoogle ScholarPubMed
Van Oosten, J. M., Peter, J., & Vandenbosch, L. (2017a). Adolescents’ sexual media use and willingness to engage in casual sex: Differential relations and underlying processes. Human Communication Research, 43(1), 127147.CrossRefGoogle Scholar
Van Oosten, J. M., Vandenbosch, L., & Peter, J. (2017b). Gender roles on social networking sites: Investigating reciprocal relationships between Dutch adolescents’ hypermasculinity and hyperfemininity and sexy online self-presentations. Journal of Children and Media, 11(2), 147166.CrossRefGoogle Scholar
Van Ouytsel, J., Ponnet, K., Walrave, M., & Temple, J. R. (2016a). Adolescent cyber dating abuse victimization and its associations with substance use, and sexual behaviors. Public Health, 135, 147151.CrossRefGoogle ScholarPubMed
Van Ouytsel, J., Van Gool, E., Walrave, M., Ponnet, K., & Peeters, E. (2016b). Exploring the role of social networking sites within adolescent romantic relationships and dating experiences. Computers in Human Behavior, 55, 7686.CrossRefGoogle Scholar
Van Ouytsel, J., Walrave, M., & Ponnet, K. (2019). Sexting within adolescents’ romantic relationships: How is it related to perceptions of love and verbal conflict? Computers in Human Behavior, 97, 216221.CrossRefGoogle Scholar
Vanden Abeele, M., Campbell, S. W., Eggermont, S., & Roe, K. (2014). Sexting, mobile porn use, and peer group dynamics: Boys' and girls' self-perceived popularity, need for popularity, and perceived peer pressure. Media Psychology, 17(1), 633.CrossRefGoogle Scholar
Vannucci, A., Simpson, E. G., Gagnon, S., & Ohannessian, C. M. (2020). Social media use and risky behaviors in adolescents: A meta-analysis. Journal of Adolescence, 79, 258274.CrossRefGoogle ScholarPubMed
Walrave, M., Heirman, W., & Hallam, L. (2014). Under pressure to sext? Applying the theory of planned behaviour to adolescent sexting. Behaviour & Information Technology, 33(1), 8698.CrossRefGoogle Scholar
Weil, L. G., Fleming, S. M., Dumontheil, I., et al. (2013). The development of metacognitive ability in adolescence. Consciousness and Cognition, 22(1), 264271.CrossRefGoogle ScholarPubMed
Williams, T., Connolly, J., Pepler, D., & Craig, W. (2009). Questioning and sexual minority adolescents: High school experiences of bullying, sexual harassment and physical abuse. Canadian Journal of Community Mental Health, 22(2), 4758.CrossRefGoogle Scholar
Wotanis, L., & McMillan, L. (2014). Performing gender on YouTube: How Jenna Marbles negotiates a hostile online environment. Feminist Media Studies, 14(6), 912928.CrossRefGoogle Scholar
Ybarra, M. L., Mitchell, K. J., Hamburger, M., Diener-West, M., & Leaf, P. J. (2011). X-rated material and perpetration of sexually aggressive behavior among children and adolescents: Is there a link? Aggressive Behavior, 37(1), 118.CrossRefGoogle ScholarPubMed
Young, R., Len-Ríos, M., & Young, H. (2017). Romantic motivations for social media use, social comparison, and online aggression among adolescents. Computers in Human Behavior, 75, 385395.CrossRefGoogle Scholar

References

Abbas, R., & Mesch, G. S. (2015). Cultural values and Facebook use among Palestinian youth in Israel. Computers in Human Behavior, 48, 644653.CrossRefGoogle Scholar
Abu Aleon, T., Weinstock, M., Manago, A. M., & Greenfield, P. M. (2019). Social change and intergenerational value differences in a Bedouin community in Israel. Journal of Cross-Cultural Psychology, 50, 708727.CrossRefGoogle Scholar
Al-Saggaf, Y. (2011). Saudi females on Facebook: An ethnographic study. International Journal of Emerging Technologies and Society, 9(1), 119.Google Scholar
Antheunis, M. L., Schouten, A. P., & Krahmer, E. (2016). The role of social networking sites in early adolescents’ social lives. Journal of Early Adolescence, 36(3), 348371.CrossRefGoogle Scholar
Antonucci, T. C. (1986). Social support networks: A hierarchical mapping technique. Generations, 10(4), 1012.Google Scholar
AP News. (2020, October 1). Egypt police arbitrarily arrest, torture LGBT people. https://bit.ly/3iGdAEUGoogle Scholar
Asemah, E. S., Ekhareafo, D. O., & Olaniran, S. (2013). Nigeria’s core values and the use of social media to promote cultural values. International Journal of Information and Communication Technology Education, 9(4), 5869.CrossRefGoogle Scholar
Bae, M. S. (2010). Go Cyworld! Korean diasporic girls producing new Korean femininity. In Mazzarella, S. R. (Ed.), Girl wide web 2.0: Revisiting girls, the internet and the negotiation of identity (pp. 91116). Peter Lang.Google Scholar
Bae-Dimitriadis, M. (2015) Performing “planned authenticity”: Diasporic Korean girls’ self-photographic play. Studies in Art Education, 56(4), 327340.CrossRefGoogle Scholar
Benjamin, R. (2019). Assessing risk, automating racism. Science, 366(6464), 421422.CrossRefGoogle ScholarPubMed
boyd, d. (2008). Facebook’s privacy trainwreck: Exposure, invasion, and social convergence. Convergence, 14(1), 1320.CrossRefGoogle Scholar
boyd, d. (2010). Social network sites as networked publics: Affordances, dynamics, and implications. In Papacharissi, Z. (Ed.), Networked self: identity, community, and culture on social network sites (pp. 3958). Routledge.Google Scholar
boyd, d. (2014). It’s complicated: The social lives of networked teens. Yale University Press.Google Scholar
Boz, N., Uhls, Y. T., & Greenfield, P. M. (2016). Cross-cultural comparison of adolescents’ online self-presentation strategies: Turkey and the United States. International Journal of Cyber Behavior, Psychology and Learning (IJCBPL), 6(3), 116.CrossRefGoogle Scholar
Brandtzaeg, P. (2012). Social networking sites: Their users and social implications – A longitudinal study. Journal of Computer-Mediated Communication, 17(4), 467488.CrossRefGoogle Scholar
Brown, G., & Michinov, N. (2017). Cultural differences in garnering social capital on Facebook: French people prefer close ties and Americans prefer distant ties. Journal of Intercultural Communication Research, 46(6), 579593.CrossRefGoogle Scholar
Burns, A. L. (2015). Self(ie)-discipline: Social regulation as enacted through the discussion of photographic practice. International Journal of Communication, 9, 17161733.Google Scholar
Cardon, P. W., Marshall, B., Choi, J., et al. (2009). Online and offline social ties of social network website users: An exploratory study in eleven societies. Journal of Computer Information Systems, 50(1), 5464.Google Scholar
Castells, M. (1996). The rise of the network society. Blackwell.Google Scholar
Chen, S. H., & Zhou, Q. (2019). Cultural values, social status, and Chinese American immigrant parents’ emotional expressivity. Journal of Cross-Cultural Psychology, 50(3), 381395.CrossRefGoogle ScholarPubMed
Cho, S. E. (2010). Cross-cultural comparison of Korean and American social network sites: Exploring cultural differences in social relationships and self-presentation [Doctoral dissertation, Rutgers University-Graduate School-New Brunswick].Google Scholar
Cole, M., & Scribner, S. (1978). Introduction. In Cole, M., Scribner, S., John-Steiner, V., & Souberman, E. (Eds.), Mind in society (pp. 115). Harvard University Press.Google Scholar
Costa, E. (2016). Social media in Southeast Turkey: Love, kinship and politics. UCL Press.Google Scholar
Costa, E. (2018). Affordances-in-practice: An ethnographic critique of social media logic and context collapse. New Media & Society, 20(10), 36413656.CrossRefGoogle ScholarPubMed
Crone, E. A., & Konijn, E. A. (2018). Media use and brain development during adolescence. Nature Communications, 9, 110.CrossRefGoogle ScholarPubMed
Culzac, N. (2014, September 17). Egypt’s police ‘using social media and apps like Grindr to trap gay people.’ Independent. https://bit.ly/2GA6ViLGoogle Scholar
Daniels, E. A., & Zurbriggen, E. L. (2016). The price of sexy: Viewers’ perceptions of a sexualized versus nonsexualized Facebook profile photograph. Psychology of Popular Media Culture, 5(1), 214.CrossRefGoogle Scholar
de León-Pasquel, L. (2018). Between romantic texting and Ethnorock on YouTube: Repertoires of identity in the virtual landscapes of Tsotsil Mayan youth. Revista LiminaR. Estudios Sociales y Humanísticos, 16(1), 4055.Google Scholar
Donath, J. (2008). Signals in social supernets. Journal of Computer-Mediated Communication, 13(1), 231251.CrossRefGoogle Scholar
Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook “friends”: Social capital and college students’ use of online social network sites. Journal of Computer-Mediated Communication, 12(4), 11431168.CrossRefGoogle Scholar
Erikson, E. H. (1963). Childhood and society (2nd ed.). Norton.Google Scholar
Fan, J. (2017, December 18). China’s selfie obsession. The New Yorker. https://www.newyorker.com/magazine/2017/12/18/chinas-selfie-obsessionGoogle Scholar
Ferguson, G. M., & Bornstein, M. H. (2012). Remote acculturation: The “Americanization” of Jamaican islanders. International Journal of Behavioral Development, 36(3), 167177.CrossRefGoogle Scholar
Ferguson, G. M., & Bornstein, M. H. (2015). Remote acculturation of early adolescents in Jamaica towards European American culture: A replication and extension. International Journal of Intercultural Relations, 45, 2435.CrossRefGoogle ScholarPubMed
French, D. (2015). Cultural templates for child and adolescent friendships. In Jensen, L. A. (Ed.), The Oxford handbook of human development and culture: An interdisciplinary perspective (pp. 425437). Oxford University Press.Google Scholar
Frenkel, S. (2018, January 2). Iranian authorities block access to social media tools. The New York Times. https://nyti.ms/3jEgv2kGoogle Scholar
Gentile, B., Twenge, J. M., Freeman, E. C., & Campbell, W. K. (2012). The effect of social networking websites on positive self-views: An experimental investigation. Computers in Human Behavior, 28(5), 19291933.CrossRefGoogle Scholar
Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 13601380.CrossRefGoogle Scholar
Greenfield, P. M. (2009). Linking social change and developmental change: Shifting pathways of human development. Developmental Psychology, 45(2), 401418.CrossRefGoogle ScholarPubMed
Gudykunst, W. B., Matsumoto, Y., Ting-Toomey, S., Nishida, T., Kim, K., & Heyman, S. (1996). The influence of cultural individualism-collectivism, self construals, and individual values on communication styles across cultures. Human Communication Research, 22(2), 510543.CrossRefGoogle Scholar
Hall, J. A., & Baym, N. K. (2012). Calling and texting (too much): Mobile maintenance expectations, (over) dependence, entrapment, and friendship satisfaction. New Media & Society, 14(2), 316331.CrossRefGoogle Scholar
Hampton, K. (2016). Persistent and pervasive community: New communication technologies and the future of community. American Behavioral Scientist, 60, 101124.CrossRefGoogle Scholar
Hansen, N., Postmes, T., Tovote, K. A., & Bos, A. (2014). How modernization instigates social change: Laptop usage as a driver of cultural value change and gender equality in a developing country. Journal of Cross-Cultural Psychology, 45(8), 12291248.CrossRefGoogle Scholar
Hansen, N., Postmes, T., van der Vinne, N., & van Thiel, W. (2012). Technology and cultural change: How ITC changes self-construal and values. Social Psychology, 43(4), 222231.CrossRefGoogle Scholar
Harman, J. P., Hansen, C. E., Cochran, M. E., & Lindsey, C. R. (2005). Liar, liar: Internet faking but not frequency of use affects social skills, self-esteem, social anxiety, and aggression. CyberPsychology & Behavior, 8, 16.CrossRefGoogle Scholar
Haynes, N. (2016). Social media in Northern Chile: Posting the extraordinarily ordinary. UCL Press.CrossRefGoogle Scholar
Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33(2–3), 6183.CrossRefGoogle ScholarPubMed
Hermans, H. J., & Dimaggio, G. (2007). Self, identity, and globalization in times of uncertainty: A dialogical analysis. Review of General Psychology, 11(1), 3161.CrossRefGoogle Scholar
Hjorth, L. (2007). Snapshots of almost contact: The rise of camera phone practices and a case study in Seoul, Korea. Continuum, 21(2), 227238.CrossRefGoogle Scholar
Hjorth, L. (2010). The game of being social: Web 2.0, social media, and online games. Iowa Journal of Communication, 42(1), 7392.Google Scholar
Hutchby, I. (2001). Technologies, texts and affordances. Sociology, 35(2), 441456.CrossRefGoogle Scholar
Internet Usage in the Middle East. (2021, May 27). Internet world stats: Usage and population statistics. Retrieved March 10, 2022, from https://bit.ly/2I8jL8rGoogle Scholar
Internet Usage Statistics. (2022, March 8). Internet world stats: Usage and population statistics. Retrieved March 10, 2022, from https://bit.ly/34xUfkyGoogle Scholar
Ito, M., Baumer, S., Bittanti, M., et al. (2009). Hanging out, messing around, and geeking out: Kids living and learning with new media. MIT Press.CrossRefGoogle Scholar
Ito, M., Matsuda, M., & Okabe, D. (2005). Personal, portable, pedestrian: Mobile phones in Japanese life. MIT Press.Google Scholar
Ito, M., & Okabe, D. (2005). Intimate visual co-presence. Paper presented at UbiComp 2005, Takanawa Prince Hotel, Tokyo, 11–14 September. http://www.itofisher.com/mito/Google Scholar
Johnson, T., Shavitt, S., & Holbrook, A. (2010). Survey response styles across cultures. In Matsumoto, D. & Van de Vijver, F. (Eds.), Cross-cultural research methods in psychology (pp. 130176). Cambridge University Press.CrossRefGoogle Scholar
Johnston, K., Tanner, M., Lalla, N., & Kawalski, D. (2013) Social capital: The benefit of Facebook ‘friends.’ Behaviour & Information Technology, 32(1), 2436.CrossRefGoogle Scholar
Kapidzic, S., & Herring, S. C. (2015). Race, gender, and self-presentation in teen profile photographs. New Media & Society, 17(6), 958976.CrossRefGoogle Scholar
Katz, J. E., & Crocker, E. T. (2015). Selfies and photo messaging as visual conversation: Reports from the United States, United Kingdom and China. International Journal of Communication, 9, 18611872.Google Scholar
Kim, H., & Papacharissi, Z. (2003). Cross-cultural differences in online self-presentation: A content analysis of personal Korean and US home pages. Asian Journal of Communication, 13(1), 100119.CrossRefGoogle Scholar
King, R. R. (2019, May 15). North Koreans want external information, but Kim Jong-Un seeks to limit access. Center for Strategic and International Studies. https://bit.ly/3nlK6jkGoogle Scholar
Kling, R. (2007). What is social informatics and why does it matter? The Information Society, 23(4), 205220.CrossRefGoogle Scholar
Lee, D. (2005). Women’s creation of camera phone culture. Fibreculture Journal, 6(6), 111.Google Scholar
Lee, D. (2010). Digital cameras, personal photography and the reconfiguration of spatial experiences. The Information Society, 26(4), 266275.CrossRefGoogle Scholar
Lee, J. Y., Park, S., Na, E., & Kim, E., (2016). A comparative study on the relationship between social networking site use and social capital among Australian and Korean youth. Journal of Youth Studies, 19(9), 11641183.CrossRefGoogle Scholar
Leskin, P. (2019, October 10). Here are all the major US tech companies blocked behind China’s ‘Great Firewall.’ Business Insider. https://bit.ly/2SxDRe8Google Scholar
Levinson, A. M., & Barron, B. (2018). Latino immigrant families learning with digital media across settings and generations. Digital Education Review, 33, 150169.CrossRefGoogle Scholar
Li, X., & Chen, W. (2014). Facebook or Renren? A comparative study of social networking site use and social capital among Chinese international students in the United States. Computers in Human Behavior, 35, 116123.CrossRefGoogle Scholar
Liu, H., Shi, J., Liu, Y., & Sheng, Z. (2013). The moderating role of attachment anxiety on social network site use intensity and social capital. Psychological Reports: Relationships & Communication, 112(1), 252265.CrossRefGoogle ScholarPubMed
Livingstone, S. (2008). Taking risky opportunities in youthful content creation: Teenagers’ use of social networking sites for intimacy, privacy and self-expression. New Media & Society, 10(3), 393411.CrossRefGoogle Scholar
Livingston, S., & Sefton-Green, J. (2016). The class: Living and learning in the digital age. New York University Press.Google Scholar
Lozada, F. T., Seaton, E. K., Williams, C. D., & Tynes, B. M. (2021). Exploration of bidirectionality in African American and Latinx adolescents’ offline and online ethnic-racial discrimination. Cultural Diversity and Ethnic Minority Psychology, 27(3), 386396.CrossRefGoogle ScholarPubMed
Madianou, M., & Miller, D. (2013). Polymedia: Towards a new theory of digital media in interpersonal communication. International Journal of Cultural Studies, 16(2), 169187.CrossRefGoogle Scholar
Manago, A. M., Graham, M. B., Greenfield, P. M., & Salimkhan, G. (2008). Self-presentation and gender on MySpace. Journal of Applied Developmental Psychology, 29(6), 446458.CrossRefGoogle Scholar
Manago, A. M., & Pacheco, P. (2019). Globalization and the transition to adulthood in a Maya community in Mexico: Communication technologies, social networks, and views on gender. [In McKenzie, J. (Ed.), Globalization as a Context for Youth Development Special Issue] New Directions for Child and Adolescent Development, 164, 1125.CrossRefGoogle Scholar
Manago, A. M., Santer, N. D., Barsigian, L. L., & Walsh, A. S. (2022). Social media as tools for cultural change in the transition to adulthood. In McLean, K. C. (Ed.), Cultural methods in psychology: Describing and transforming cultures (pp. 146173). Oxford University Press.Google Scholar
Manago, A., Taylor, T., & Greenfield, P. (2012). Me and my 400 friends: The anatomy of college students’ Facebook networks, their communication patterns, and well-being. Developmental Psychology, 48(2), 369380.CrossRefGoogle ScholarPubMed
Manago, A. M., & Vaughn, L. (2015). Social media, friendship and happiness in the millennial generation. In Demir, M. (Ed.), Friendship and happiness: Across the lifespan and in different cultures (pp. 187206). Springer.CrossRefGoogle Scholar
Mariek, M. P., Vanden, A., Marjolijn, L. A., et al. (2018). Does Facebook use predict college students’ social capital? A replication of Ellison, Steinfield, and Lampe’s (2007) study using the original and more recent measures of Facebook use and social capital. Communication Studies, 69(3), 272282.Google Scholar
Marston, K. (2019). Researching LGBT+ youth intimacies and social media: The strengths and limitations of participant-led visual methods. Qualitative Inquiry, 25(3), 278288.CrossRefGoogle Scholar
Marwick, A. (2012). The public domain: Surveillance in everyday life. Surveillance & Society, 9(4), 378393.CrossRefGoogle Scholar
Marwick, A. E., & boyd, d. (2011). I tweet honestly, I tweet passionately: Twitter users, context collapse, and the imagined audience. New Media & Society, 13(1), 114133.CrossRefGoogle Scholar
Marwick, A. E., & boyd, d. (2014). Networked privacy: How teenagers negotiate context in social media. New Media & Society, 16(7), 10511067.CrossRefGoogle Scholar
Mascheroni, G., & Vincent, J. (2016). Perpetual contact as a communicative affordance: Opportunities, constraints, and emotions. Mobile Media & Communication, 4(3), 310326.CrossRefGoogle Scholar
Maynard, A. E., Subrahmanyam, K., & Greenfield, P. M. (2005). Technology and the development of intelligence. In Sternberg, R. J. & Preiss, D. (Eds.), Intelligence and technology: The impact of tools on the nature and development of human abilities (pp. 5497). Erlbaum.Google Scholar
McCain, J. L., & Campbell, W. K. (2018). Narcissism and social media use: A meta-analytic review. Psychology of Popular Media Culture, 7(3), 308327.CrossRefGoogle Scholar
McDonald, T. (2016). Social media in Rural China. UCL Press.CrossRefGoogle Scholar
McKenna, K. Y. A., & Bargh, J. A. (2000). Plan 9 from Cyberspace: The implications of the internet for personality and social psychology. Personality and Social Psychology Review, 4(1), 5775.CrossRefGoogle Scholar
McKenzie, J. (2019). Shifting practices, shifting selves: Negotiations of local and global cultures among adolescents in northern Thailand. Child Development, 90(6), 20352052.CrossRefGoogle ScholarPubMed
McKenzie, J. (2020). Negotiating local and global values in a globalized world: The envisioned futures of Thai adolescents. Journal of Research on Adolescence, 30(4), 856874.CrossRefGoogle Scholar
McKenzie, J., Castellón, R., Willis-Grossmann, E., Landeros, C., Rooney, J., & Stewart, C. (2022). Digital divides and dyadic gaps: A portrait of media use and perspectives of media in Thailand. [Under review].Google Scholar
McKenzie, J., Rooney, J., Stewart, C., Castellón, R., Landeros, C., & Willis, E. (2019). Brokering culture and power in a media-driven world: Parents of adolescents in northern Thailand. Journal of Cross-Cultural Psychology, 50, 972990.CrossRefGoogle Scholar
Mesch, G. S. (2006). The family and the internet: Exploring a social boundaries approach. Journal of Family Communication, 6(2), 119138.CrossRefGoogle Scholar
Michikyan, M., Dennis, J., & Subrahmanyam, K. (2015). Can you guess who I am? Real, ideal, and false self-presentation on Facebook among emerging adults. Emerging Adulthood, 3(1), 5564.CrossRefGoogle Scholar
Miller, D., Sinanan, J., Wang, X., et al. (2016). How the world changed social media. UCL Press.CrossRefGoogle Scholar
Mishra, S., & Basu, S. (2014). Family honor, cultural norms, and social networking: Strategic choices in the visual self-presentation of young Indian Muslim women. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 8, Article 3.CrossRefGoogle Scholar
Nemer, D., & Freeman, G. (2015). Empowering the marginalized: Rethinking selfies in the slums of Brazil. International Journal of Communication, 9(1), 18321847.Google Scholar
Nicolescu, R. (2016). Social media in Southeast Italy. UCL Press.CrossRefGoogle Scholar
Ortiz-Ospina, E. (2017, February 22). Children and poverty: Evidence from new World Bank data. Our World in Data. https://bit.ly/33CjpPuGoogle Scholar
Pathak-Shelat, M., & DeShano, C. (2014). Digital youth cultures in small town and rural Gujarat, India. New Media & Society, 16(6), 9831001.CrossRefGoogle Scholar
Pew Research Center. (2019, June 12). Social media fact sheet. https://pewrsr.ch/30KLm5VGoogle Scholar
Phua, J., Jin, S. V., & Kim, J. J. (2017). Uses and gratifications of social networking sites for bridging and bonding social capital: A comparison of Facebook, Twitter, Instagram, and Snapchat. Computers in Human Behavior, 72, 115122.CrossRefGoogle Scholar
Poushter, J., Bishop, C., & Chwe, H. (2018, June 19). Social media use continues to rise in developing countries but plateaus across developed ones. Pew Research Center. https://pewrsr.ch/30KH3rhGoogle Scholar
Quinn, N. (2019). Historical circumstances and biological proclivities surrounding patriarchy. In Mathews, H. & Manago, A. (Eds.), The psychology of women under patriarchy (pp. 3150). SAR Press.Google Scholar
Rainie, L., & Wellman, B. (2012). Networked: The new social operating system. MIT Press.CrossRefGoogle Scholar
Rao, M. A., Berry, R., Gonsalves, A., Hastak, Y., Shah, M., & Roeser, R. W. (2013). Globalization and the identity remix among urban adolescents in India. Journal of Research on Adolescence, 23(1), 924.CrossRefGoogle Scholar
Raza, S. A., Qazi, W., & Umer, A. (2017). Facebook is a source of social capital building among university students: Evidence from a developing country. Journal of Educational Computing Research, 55(3), 295322.CrossRefGoogle Scholar
Reisinger, D. (2019, February 22). China banned 23 million people from traveling last year for poor ‘social credit’ scores. Fortune. https://bit.ly/30Ip9FvGoogle Scholar
Rickman, A. (2018). Adolescence, girlhood, and media migration: US teens’ use of social media to negotiate offline struggles. Lexington Books.Google Scholar
Rideout, V. J., & Katz, V. S. (2016). Opportunity for all? Technology and learning in lower-income families. A report of the Families and Media Project. The Joan Ganz Cooney Center at Sesame Workshop.Google Scholar
Rodino, M. (1997). Breaking out of binaries: Reconceptualizing gender and its relationship to language in computer-mediated communication. Journal of Computer-Mediated Communication, 3(3), JCMC333.Google Scholar
Rubin, J. D., & McClelland, S. I. (2015). ‘Even though it’s a small checkbox, it’s a big deal’: Stresses and strains of managing sexual identity(s) on Facebook. Culture, Health & Sexuality, 17(4), 512526.CrossRefGoogle ScholarPubMed
Senft, T. M., & Baym, N. K. (2015). What does the selfie say? Investigating a global phenomenon. International Journal of Communication, 9, 15881606.Google Scholar
Schrobsdorff, S. (2016, October 27). Teen depression and anxiety: Why the kids are not alright. Time. https://bit.ly/3jGr4SvGoogle Scholar
Shane-Simpson, C., Manago, A., Gaggi, N., & Gillespie-Lynch, K. (2018). Why do college students prefer Facebook, Twitter, or Instagram? Site affordances, tensions between privacy and self-expression, and implications for social capital. Computers in Human Behavior, 86, 276288.CrossRefGoogle Scholar
Sheldon, P., Herzfeldt, E., & Rauschnabel, P. A. (2020). Culture and social media: The relationship between cultural values and hashtagging styles. Behaviour & Information Technology, 39(7), 758770.CrossRefGoogle Scholar
Shen, K. N., & Khalifa, M. (2010). Facebook usage among Arabic college students: Preliminary findings on gender differences. University of Wollongong Research Online. http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1074&context=dubaipapersGoogle Scholar
Silver, L., Smith, A., Johnson, C., Jiang, J., Anderson, M., & Rainie, L. (2019, March 7). Use of smartphones and social media is common across most emerging economies. Pew Research Center. https://pewrsr.ch/2SCSMDLGoogle Scholar
Sinanan, J. (2017). Social media in Trinidad. UCL Press.CrossRefGoogle Scholar
Subrahmanyam, K., Reich, S. M., Waechter, N., & Espinoza, G. (2008). Online and offline social networks: Use of social networking sites by emerging adults. Journal of Applied Developmental Psychology, 29(6), 420433.CrossRefGoogle Scholar
Sugimura, K. (2020). Adolescent identity development in Japan. Child Development Perspectives, 14(2), 7177.CrossRefGoogle Scholar
Thomson, R., Yuki, M., & Ito, N. (2015). A socio-ecological approach to national differences in online privacy concern: The role of relational mobility and trust. Computers in Human Behavior, 51, 285292.CrossRefGoogle Scholar
Tifferet, S., & Vilnai-Yavetz, I. (2014). Gender differences in Facebook self-presentation: An international randomized study. Computers in Human Behavior, 35, 388399.CrossRefGoogle Scholar
Tsai, J. L. (2017). Ideal affect in daily life: Implications for affective experience, health, and social behavior. Current Opinion in Psychology, 17, 118128.CrossRefGoogle ScholarPubMed
Tufekci, Z. (2008). Can you see me now? Audience and disclosure regulation in online social network sites. Bulletin of Science Technology & Society, 28(1), 2036.CrossRefGoogle Scholar
Turkle, S. (1997). Life on the screen: Identity in the age of the internet. Simon & Schuster.Google Scholar
Twenge, J. M. (2013). Does online social media lead to social connection or social disconnection?. Journal of College and Character, 14(1), 1120.CrossRefGoogle Scholar
Twenge, J. M. (2017). iGen: Why today’s super-connected kids are growing up less rebellious, less happy – and completely unprepared for adulthood. Simon & Schuster.Google Scholar
Twenge, J. M., Martin, G. N., & Spitzberg, B. H. (2019). Trends in U.S. adolescents’ media use, 1976–2016: The rise of digital media, the decline of TV, and the (near) demise of print. Psychology of Popular Media Culture, 8(4), 329345.CrossRefGoogle Scholar
Tynes, B. M., Garcia, E. L., Giang, M. T., & Coleman, N. E. (2011). The racial landscape of social network sites: Forging identity, community, and civic engagement. I/S: A Journal of Law and Policy for the Information Society, 7, 71100.Google Scholar
Valkenburg, P., & Peter, J. (2008). Adolescents’ identity experiments on the internet: Consequences for social competence and self-concept unity. Communication Research, 35(2), 208231.CrossRefGoogle Scholar
Vanden Abeele, M. M. P. (2016). Mobile lifestyles: Conceptualizing heterogeneity in mobile youth culture. New Media & Society, 18(6), 908926.CrossRefGoogle Scholar
Venkatraman, S. (2016). Social media in South India. UCL Press.Google Scholar
Vitak, J. (2012). The impact of context collapse and privacy on social network site disclosures. Journal of Broadcasting & Electronic Media, 56(4), 451470.CrossRefGoogle Scholar
Wang, X. (2016). Social media in industrial China. UCL Press.CrossRefGoogle Scholar
Weisner, T. S. (2014). Culture, context, and child well-being. In Ben-Arieh, A., Casas, F., Frønes, I., & Korbin, J. (Eds.), Handbook of child well-being (pp. 87103). Springer.CrossRefGoogle Scholar
Wellman, B. (2002). Little boxes, globalization, and networked individualism. In Tanabe, M., van den Besselaar, P., & Ishida, T. (Eds.), Digital cities II: Computational and sociological approaches (pp. 1025). Springer.CrossRefGoogle Scholar
Williams, D. (2006). On and off the ’net: Scales for social capital in an online era. Journal of Computer-Mediated Communication, 11(2), 593628.CrossRefGoogle Scholar
Wolff, J. (2019, December 9). Iran cutting off its internet wasn’t a show of strength. It was a sign of panic. The Washington Post. https://wapo.st/2GIiEf0Google Scholar
Wong, K. L. X., & Dobson, A. S. (2019). We’re just data: Exploring China’s social credit system in relation to digital platform ratings cultures in Westernised democracies. Global Media and China, 4(2), 220232.CrossRefGoogle Scholar
World Bank Country and Lending Groups. (n.d.). The World Bank. https://bit.ly/3d6b21XGoogle Scholar
Xu, Q., & Armstrong, C. L. (2019). # SELFIES at the 2016 Rio Olympics: Comparing self-representations of male and female athletes from the US and China. Journal of Broadcasting & Electronic Media, 63(2), 322338.CrossRefGoogle Scholar
Yang, C. C., & Brown, B. B. (2016). Online self-presentation on Facebook and self development during the college transition. Journal of Youth and Adolescence, 45(2), 402416.CrossRefGoogle ScholarPubMed
Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. Public Affairs Books.Google Scholar

References

Abidin, C., & Zeng, J. (2020). Feeling Asian together: Coping with #COVIDRacism on subtle Asian traits. Social Media + Society, 6(3). https://doi.org/10.1177/2056305120948223CrossRefGoogle ScholarPubMed
Anderson, M., & Jiang, J. (2018, May 31). Teens, social media & technology 2018. Pew Research Center: Internet, Science & Tech. https://www.pewresearch.org/internet/2018/05/31/teens-social-media-technology-2018/Google Scholar
Barman-Adhikari, A., Rice, E., Bender, K., Lengnick-Hall, R., Yoshioka-Maxwell, A., & Rhoades, H. (2016). Social networking technology use and engagement in HIV-related risk and protective behaviors among homeless youth. Journal of Health Communication, 21(7), 809817. https://doi.org/10.1080/10810730.2016.1177139CrossRefGoogle ScholarPubMed
Berry, G. L. (2000). Multicultural media portrayals and the changing demographic landscape: The psychosocial impact of television representations on the adolescent of color. Journal of Adolescent Health, 27(2, Suppl. 1), 5760. https://doi.org/10.1016/S1054-139X(00)00133-6CrossRefGoogle ScholarPubMed
Borough, M., Literat, I., & Ikin, A. (2020). “Good social media?”: Underrepresented youth perspectives on the ethical and equitable design of social media platforms. Social Media + Society, 6(2). https://doi.org/10.1177/2056305120928488Google Scholar
Calzo, J. P., & Blashill, A. J. (2018). Child sexual orientation and gender identity in the adolescent brain cognitive development cohort study. JAMA Pediatrics, 172(11), 10901092. https://doi.org/10.1001/jamapediatrics.2018.2496CrossRefGoogle ScholarPubMed
Carey, E. (2020, October 1). TikTok’s Queer “It Girls” are creating new LGBTQ+ safe spaces. them. https://www.them.us/story/tiktoks-queer-it-girls-create-lgbtq-safe-spacesGoogle Scholar
Caton, S., & Chapman, M. (2016). The use of social media and people with intellectual disability: A systematic review and thematic analysis. Journal of Intellectual & Developmental Disability, 41(2), 125139. https://doi.org/10.3109/13668250.2016.1153052CrossRefGoogle Scholar
Cavalcante, A. (2019). Tumbling into queer utopias and vortexes: Experiences of LGBTQ social media users on Tumblr. Journal of Homosexuality, 66(12), 17151735. https://doi.org/10.1080/00918369.2018.1511131CrossRefGoogle ScholarPubMed
Ceglarek, P., & Ward, L. (2016). A tool for help or harm? How associations between social networking use, social support, and mental health differ for sexual minority and heterosexual youth. Computers in Human Behavior, 65, 201209. https://doi.org/10.1016/j.chb.2016.07.051CrossRefGoogle Scholar
Charmaraman, L., Chan, H. B., Chen, S., Richer, A., & Ramanudom, B. (2018). Asian American social media use: From cyber dependence and cyber harassment to saving face. Asian American Journal of Psychology, 9(1), 7286. https://doi.org/10.1037/aap0000109CrossRefGoogle Scholar
Charmaraman, L., Chan, H., Price, T., & Richer, A. (2015). Women of color cultivating virtual social capital: Surviving and thriving. In Tassie, K. E. & Brown, S. M. (Eds.), Women of color and social media multitasking: Blogs, timelines, feeds, and community (pp. 119). Lexington Books.Google Scholar
Charmaraman, L., & Grossman, J. M. (2010). Importance of race and ethnicity: An exploration of Asian, Black, Latino, and multiracial adolescent identity. Cultural Diversity and Ethnic Minority Psychology, 16(2), 144151. https://doi.org/10.1037/a0018668CrossRefGoogle ScholarPubMed
Charmaraman, L., Grossman, J. M., & Richer, A. M. (2021). Same-sex attraction disclosure and sexual communication topics within families. Journal of GLBT Family Studies, 17(2), 118134. https://doi.org/10.1080/1550428X.2020.1820414CrossRefGoogle ScholarPubMed
Charmaraman, L., Hodes, R., & Richer, A. (2021). Young sexual minority adolescent experiences of self-expression and isolation on social technology: A cross-sectional survey. JMIR Mental Health, 8(9), e26207. https://doi.org/10.2196/26207CrossRefGoogle Scholar
Cheadle, J. E., & Whitbeck, L. B. (2011). Alcohol use trajectories and problem drinking over the course of adolescence: A study of North American Indigenous youth and their caretakers. Journal of Health and Social Behavior, 52(2), 228245. https://doi.org/10.1177/0022146510393973CrossRefGoogle ScholarPubMed
Cooper, R. M., & Blumenfeld, W. J. (2012). Responses to cyberbullying: A descriptive analysis of the frequency of and impact on LGBT and allied youth. Journal of LGBT Youth, 9(2), 153177. https://doi.org/10.1080/19361653.2011.649616CrossRefGoogle Scholar
Croucher, S. M., Nguyen, T., & Rahmani, D. (2020). Prejudice toward Asian Americans in the Covid-19 pandemic: The effects of social media use in the United States. Frontiers in Communication, 5(39). https://doi.org/10.3389/fcomm.2020.00039CrossRefGoogle Scholar
Davidson, J. (2008). Autistic culture online: Virtual communication and cultural expression on the spectrum. Social Cultural Geography, 9(7), 791806. https://doi.org/10.1080/14649360802382586CrossRefGoogle Scholar
DeVito, M. A., Walker, A. M., & Birnholtz, J. (2018). “Too gay for Facebook”: Presenting LGBTQ+ identity throughout the personal social media ecosystem. Proceedings of the ACM on Human-Computer Interaction, 2(44), 123. https://doi.org/10.1145/3274313CrossRefGoogle Scholar
Eyrich-Garg, K. M. (2010). Mobile phone technology: A new paradigm for the prevention, treatment, and research of the non-sheltered “street” homeless? Journal of Urban Health, 87(3), 365380. https://doi.org/10.1007/s11524-010-9456-2CrossRefGoogle ScholarPubMed
Florini, S. (2014). Tweets, tweeps, and signifyin’: Communication and cultural performance on “Black Twitter.Television & New Media, 15(3), 223237. https://doi.org/10.1177/1527476413480247CrossRefGoogle Scholar
Fox, J., & Ralston, R. (2016). Queer identity online: Informal learning and teaching experiences of LGBTQ individuals on social media. Computers in Human Behavior, 65, 635642. https://doi.org/10.1016/j.chb.2016.06.009CrossRefGoogle Scholar
Gee, G. C., Ro, A., Shariff-Marco, S., & Chae, D. (2009). Racial discrimination and health among Asian Americans: Evidence, assessment, and directions for future research. Epidemiologic Reviews, 31(1), 130151. https://doi.org/10.1093/epirev/mxp009CrossRefGoogle ScholarPubMed
George, M. J., Jensen, M. R., Russell, M. A., et al. (2020). Young adolescents’ digital technology use, perceived impairments, and well-being in a representative sample. Journal of Pediatrics, 219, 180187. https://doi.org/10.1016/j.jpeds.2019.12.002CrossRefGoogle ScholarPubMed
Ghaziani, A. (2014). There goes the gayborhood? Princeton University Press.CrossRefGoogle Scholar
Gordon, L. E., & Silva, T. J. (2014). Inhabiting the sexual landscape: Toward an interpretive theory of the development of sexual orientation and identity. Journal of Homosexuality, 62(4), 495530. https://doi.org/10.1080/00918369.2014.986417CrossRefGoogle Scholar
Grasmuck, S., Martin, J., & Zhao, S. (2009). Ethno-racial identity displays on Facebook. Journal of Computer-Mediated Communication, 15(1), 158188. https://doi.org/10.1111/j.1083-6101.2009.01498.xCrossRefGoogle Scholar
Guadagno, R. E., Muscanell, N. L., & Pollio, D. E. (2013). The homeless use Facebook?! Similarities of social network use between college students and homeless young adults. Computers in Human Behavior, 29(1), 8689. https://doi.org/10.1016/j.chb.2012.07.019CrossRefGoogle Scholar
Hargittai, E., & Hinnant, A. (2008) Digital inequality differences in young adults’ use of the internet. Communication Research, 35(5), 602621. https://doi.org/10.1177/0093650208321782CrossRefGoogle Scholar
Heiman, T., Olenik-Shemesh, D., & Eden, S. (2014). Cyberbullying involvement among students with ADHD: Relation to loneliness, self-efficacy, and social support. European Journal of Special Needs Education, 30(1), 1529. https://doi.org/10.1080/08856257.2014.943562CrossRefGoogle Scholar
Hernandez, J. M., & Charmaraman, L. (2021). Conceptualizing the role of racial-ethnic identity in US adolescent social technology use and wellbeing. [Unpublished manuscript].Google Scholar
Hillier, L., & Harrison, L. (2007). Building realities less limited than their own: Young people practicing same-sex attraction on the internet. Sexualities, 10(1), 82100. https://doi.org/10.1177/1363460707072956CrossRefGoogle Scholar
Holmes, K. M., & O’Loughlin, N. (2014). The experiences of people with learning disabilities on social networking sites. British Journal of Learning Disabilities, 42(1), 37. https://doi.org/10.1111/bld.12001CrossRefGoogle Scholar
Ito, M., Odgers, C., Schueller, S., et al. (2020). Social media and youth wellbeing: What we know and where we could go. Connected Learning Alliance.Google Scholar
Kiang, L., Witkow, M. R., & Champagne, M. C. (2013). Normative changes in ethnic and American identities and links with adjustment among Asian American adolescents. Developmental Psychology, 49(9), 17131722. https://doi.org/10.1037/a0030840CrossRefGoogle ScholarPubMed
Kowalski, R. M., Morgan, C. A., Drake-Lavelle, K., & Allison, B. (2016). Cyberbullying among college students with disabilities. Computers in Human Behavior, 57, 416427. https://doi.org/10.1016/j.chb.2015.12.044CrossRefGoogle Scholar
Kuper, L. E., Wright, L., & Mustanski, B. (2018). Gender identity development among transgender and gender nonconforming emerging adults: An intersectional approach. International Journal of Transgenderism, 19(4), 436455. https://doi.org/10.1080/15532739.2018.1443869CrossRefGoogle Scholar
Kydland, F., Molka-Danielsen, J., & Balandin, S. (2012). Examining the use of social media tool ‘Flickr’ for impact on loneliness for people with intellectual disability. In Fallmyr, T. (Ed.), NOKOBIT2012: Proceedings of the 2012 Norsk konferanse for organisasjoners bruk av informasjonsteknologi (pp. 253264). Akademika forlag.Google Scholar
Löfgren-Mårtenson, L. (2008). Love in cyberspace: Swedish young people with intellectual disabilities and the internet. Scandinavian Journal of Disability Research, 10(2), 125138. https://doi.org/10.1080/15017410701758005CrossRefGoogle Scholar
Madden, M., Lenhart, A., Duggan, M., Cortesi, S., & Gasser, U. (2013). Teens and technology 2013. Pew Research Center. http://www.pewinternet.org/~/media//Files/Reports/2013/PIP_Teensand Technology2013.pdfGoogle Scholar
Manago, A. M. (2015). Media and the development of identity. In Scott, R. & Kosslyn, S. (Eds.), Emerging trends in the social and behavioral sciences (pp. 114). Wiley & Sons, Inc.Google Scholar
Masten, A. S., & Reed, M. G. J. (2002). Resilience in development. In Snyder, C. R., & Lopez, S. J. (Eds.), Handbook of positive psychology (pp. 7488). Oxford University Press.Google Scholar
Mayhew, A. & Weigle, P. (2018). Media engagement and identity formation among minority youth. Child and Adolescent Psychiatric Clinics of North America, 27(2), 269285. https://doi.org/10.1016/j.chc.2017.11.012CrossRefGoogle ScholarPubMed
McClimens, A., & Gordon, F. (2008). Presentation of self in everyday life: How people labelled with intellectual disability manage identity as they engage the blogosphere. Sociological Research Online, 13(4), 1. https://doi.org/10.5153/sro.1774CrossRefGoogle Scholar
McConnell, E., Néray, B., Hogan, B., Korpak, A., Clifford, A., & Birkett, M. (2018). “Everybody puts their whole life on Facebook”: Identity management and the online social networks of LGBTQ youth. International Journal of Environmental Research and Public Health, 15(6), Article 1078. https://doi.org/10.3390/ijerph15061078CrossRefGoogle ScholarPubMed
Monkman, L. (2020, April 13). First Nations TikTok users hope to inspire youth to learn more about their cultures. CBC. https://www.cbc.ca/news/indigenous/tiktok-inspire-indigenous-youth-1.5528667Google Scholar
Noor, P. (2020, July 1). The Navajo teenager who went viral reporting on coronavirus: “I just want us to be seen.” The Guardian. https://www.theguardian.com/us-news/2020/may/22/navajo-teenager-tiktok-reporting-coronavirusGoogle Scholar
Odgers, C. (2018). Smartphones are bad for some teens, not all. Nature, 554, 432434.CrossRefGoogle ScholarPubMed
Odgers, C. L., & Jensen, M. R. (2020). Annual research review: Adolescent mental health in the digital age: Facts, fears, and future directions. Journal of Child Psychology and Psychiatry, 61(3), 336348. https://doi.org/10.1111/jcpp.13190CrossRefGoogle ScholarPubMed
Odgers, C., & Robb, M. B. (2020). Tweens, teens, tech, and mental health: Coming of age in an increasingly digital, uncertain, and unequal world, 2020. Common Sense Media.Google Scholar
OECD. (2016). Are there differences in how advantaged and disadvantaged students use the internet? http://dx.doi.org/10.1787/5jlv8zq6hw43-enCrossRefGoogle Scholar
Ohlheiser, A. (2020, January 28). TikTok has become the soul of the LGBTQ Internet. Washington Post. https://www.washingtonpost.com/technology/2020/01/28/tiktok-has-become-soul-lgbtq-internet/Google Scholar
Oldenburg, R. (1989). The great good place: Cafés, coffee shops, community centers, beauty parlors, general stores, bars, hangouts, and how they get you through the day. Paragon House.Google Scholar
Palmer, N. A., Kosciw, J. G., Greytak, E. A., Ybarra, M. L., Korchmaros, J., & Mitchell, K. J. (2013). Out online: The experiences of lesbian, gay, bisexual and transgender youth on the internet. GLSEN.Google Scholar
Panizo, L. C. (2018). Gay teenagers in the digital age: Orientations for educators. Alteridad. Revista de Educación, 14(1), 6272. https://doi.org/10.17163/alt.v14n1.2019.05Google Scholar
Park-Lee, E., Lipari, R. N., Bose, J., et al. (2018, July). Substance use and mental health issues among U.S.-born American Indians or Alaska Natives residing on and off tribal lands. Center for Behavioral Health Statistics and Quality. https://www.samhsa.gov/data/sites/default/files/cbhsq-reports/DRAIANTribalAreas2018/DRAIANTribalAreas2018.pdfGoogle Scholar
Pew Research Center. (2013). A survey of LGBT Americans. Retrieved April 11, 2021. https://www.pewsocialtrends.org/2013/06/13/a-surveyof-lgbt-americans/Google Scholar
Phinney, J. S. (1990). Ethnic identity in adolescents and adults: Review of research. Psychological Bulletin, 108(3), 499514. https://doi.org/10.1037/0033-2909.108.3.499CrossRefGoogle ScholarPubMed
Phinney, J. S., & Ong, A. D. (2007). Conceptualization and measurement of ethnic identity: Current status and future directions. Journal of Counseling Psychology, 54(3), 271281. https://doi.org/10.1037/0022-0167.54.3.271CrossRefGoogle Scholar
Pollio, D. E., Batey, D. S., Bender, K., Ferguson, K., & Thompson, S. (2013). Technology use among emerging adult homeless in two US cities. Social Work, 58(2), 173175. https://doi.org/10.1093/sw/swt006CrossRefGoogle Scholar
Przybylski, A. K., & Bowes, L. (2017). Cyberbullying and adolescent well-being in England: A population-based cross-sectional study. The Lancet Child & Adolescent Health, 1(1), 1926. https://doi.org/10.1016/S2352-4642(17)30011-1CrossRefGoogle Scholar
Quintana, S. M. (1994). A model of ethnic perspective-taking ability applied to Mexican-American children and youth. International Journal of Intercultural Relations, 18(4), 419448. https://doi.org/10.1016/0147-1767(94)90016-7CrossRefGoogle Scholar
Rice, E., Lee, A., & Taitt, S. (2011). Cell phone use among homeless youth: Potential for new health interventions and research. Journal of Urban Health, 88(6), 11751182. https://doi.org/10.1007/s11524-011-9624-zCrossRefGoogle ScholarPubMed
Rice, E., Milburn, N. G., Rotheram-Borus, M. J., Mallett, S., & Rosenthal, D. (2005). The effects of peer group network properties on drug use among homeless youth. The American Behavioral Scientist, 48(8), 11021123. https://doi.org/10.1177/0002764204274194CrossRefGoogle ScholarPubMed
Robards, B., Churchill, B., Vivienne, S., Hanckel, B., & Byron, P. (2019). Twenty years of ‘cyberqueer’: The enduring significance of the internet for young LGBTIQ+ people. In Aggleton, P., Cover, R., Leahy, D., Marshall, D., & Rasmussen, M. L. (Eds.), Youth, sexuality, and sexual citizenship (pp. 151167). Routledge. https://doi.org/10.4324/9781351214742-15Google Scholar
Robertson, M. A. (2013). “How do I know I am gay?”: Understanding sexual orientation, identity and behavior among adolescents in an LGBT youth center. Sexuality and Culture, 18(1), 385404. https://doi-org.ezproxy.wellesley.edu/10.1007/s12119–013-9203-4CrossRefGoogle Scholar
Romano, A. (2019, October 10). A group of YouTubers is trying to prove the site systematically demonetizes queer content. Vox. https://www.vox.com/culture/2019/10/10/20893258/youtube-lgbtq-censorship-demonetization-nerd-city-algorithm-reportGoogle Scholar
Rosario, M., Schrimshaw, E. W., & Hunter, J. (2008). Predicting different patterns of sexual identity development over time among lesbian, gay, and bisexual youths: A cluster analytic approach. American Journal of Community Psychology, 42(3–4), 266282. https://doi-org.ezproxy.wellesley.edu/10.1007/s10464–008-9207-7CrossRefGoogle ScholarPubMed
Rushing, S. C., & Stephens, D. (2011). Use of media technologies by Native American teens and young adults in the pacific northwest: Exploring their utility for designing culturally appropriate technology-based health interventions. The Journal of Primary Prevention, 32(3), Article 135. https://doi.org/10.1007/s10935-011-0242-zCrossRefGoogle Scholar
Rushing, S. N., Stephens, D., & Dog, T. L. G. (2018). We R Native: Harnessing technology to improve health outcomes for American Indian and Alaska Native youth. Journal of Adolescent Health, 62(2), S83S84. https://doi.org/10.1016/j.jadohealth.2017.11.168CrossRefGoogle Scholar
Salk, R. H., Thoma, B. C., & Choukas-Bradley, B. (2020). The gender minority youth study: Overview of methods and social media recruitment of a nationwide sample of U.S. cisgender and transgender adolescents. Archives of Sexual Behavior, 49(7), 26012610. https://doi.org/10.1007/s10508-020-01695-xCrossRefGoogle ScholarPubMed
Schimmel-Bristow, A., & Ahrens, K. R. (2018). Technology use among special populations. In Moreno, M. A. & Radovic, A. (Eds.), Technology and adolescent mental health (pp. 4355). Springer. https://doi.org/10.1007/978-3-319-69638-6Google Scholar
Scroggs, B., & Vennum, A. (2020). Gender and sexual minority group identification as a process of identity development during emerging adulthood. Journal of LGBT Youth, 18(3), 287304. https://doi.org/10.1080/19361653.2020.1722780CrossRefGoogle Scholar
Seale, J. K., (2007). Strategies for supporting the online publishing activities of adults with learning difficulties. Disability & Society, 22(2), 173186. https://doi.org/10.1080/09687590601141626CrossRefGoogle Scholar
Simpson, E., & Semaan, B. (2020). For you, or for “you”?: Everyday LGBTQ+ encounters with TikTok. Proceedings of the ACM on Human-Computer Interaction, 4(252), 134. https://doi.org/10.1145/3432951CrossRefGoogle Scholar
Soukup, C. (2006). Computer-mediated communication as a digital third place: Building Oldenburg’s great good places on the world wide web. New Media & Society, 8(3), 421440. https://doi.org/10.1177/1461444806061953CrossRefGoogle Scholar
Spencer, M. B., Dupree, D., & Hartmann, T. (1997). A phenomenological variant of ecological systems theory (PVEST): A self organization perspective in context. UPenn. http://repository.upenn.edu/gse_pubs/4Google ScholarPubMed
Spooner, T. (2001, December 12). Asian-Americans and the internet. Pew Research Center: Internet, Science & Tech. https://www.pewresearch.org/internet/2001/12/12/asian-americans-and-the-internet/Google Scholar
Stephan, W., & Stephan, C. W. (2000). An integrated threat theory of prejudice. In Oskamp, S. (Ed.). Reducing prejudice and discrimination (pp. 2346). Lawrence Erlbaum Associates.Google Scholar
Stevens, R., Gilliard-Matthews, S., Dunaev, J., Woods, M. K., & Brawner, B. M. (2017). The digital hood: Social media use among youth in disadvantaged neighborhoods. New Media & Society, 19(6), 950967. https://doi.org/10.1177/1461444815625941CrossRefGoogle ScholarPubMed
Stewart, S., Riecken, T., Scott, T., Tanaka, M., & Riecken, J. (2008). Expanding health literacy: Indigenous youth creating videos. Journal of Health Psychology, 13(2), 180189. https://doi.org/10.1177/1359105307086709CrossRefGoogle ScholarPubMed
Subrahmanyam, K., Smahel, D., & Greenfield, P. (2006). Connecting developmental constructions to the internet: Identity presentation and sexual exploration in online teen chat rooms. Developmental Psychology, 42, 395406. https://doi.org/10.1037/0012-1649.42.3.395CrossRefGoogle Scholar
Surgeon General. (2001). Mental health: Culture, race, ethnicity. Supplement to mental health: A report of the Surgeon General. US Government Printing Office.Google Scholar
Sybert, J. (2021). The demise of #NSFW: Contested platform governance and Tumblr’s 2018 adult content ban. New Media & Society. https://doi.org/10.1177/1461444821996715CrossRefGoogle Scholar
Trent, M., Dooley, D. G., & Dougé, J. (2019). The impact of racism on child and adolescent health. Pediatrics, 144(2), e20191765. https://doi.org/10.1542/peds.2019-1765CrossRefGoogle ScholarPubMed
Tynes, B. M., English, D., Del Toro, J., Smith, N. A., Lozada, F. T., & Williams, D. R. (2020). Trajectories of online racial discrimination and psychological functioning among African American and Latino adolescents. Child Development, 91(5), 15771593. https://doi.org/10.1111/cdev.13350CrossRefGoogle ScholarPubMed
Uba, L. (1994). Asian Americans: Personality patterns, identity, and mental health. Guilford Press.Google Scholar
Umaña-Taylor, A. J., Quintana, S. M., Lee, R. M., et al. (2014). Ethnic and racial identity during adolescence and into young adulthood: An integrated conceptualization. Child Development, 85(1), 2139. https://doi.org/10.1111/cdev.12196CrossRefGoogle ScholarPubMed
Umaña-Taylor, A. J., Tynes, B. M., Toomey, R. B., Williams, D. R., & Mitchell, K. J. (2015). Latino adolescents’ perceived discrimination in online and offline settings: An examination of cultural risk and protective factors. Developmental Psychology, 51(1), 87100. https://doi.org/10.1037/a0038432CrossRefGoogle ScholarPubMed
US Census Bureau. (2019, October 2). Population estimates show aging across race groups differs. https://www.census.gov/newsroom/press-releases/2019/estimates-characteristics.htmlGoogle Scholar
Varjas, K., Meyers, J., Kiperman, S., & Howard, A. (2013). Technology hurts? Lesbian, gay, and bisexual youth perspectives of technology and cyberbullying. Journal of School Violence, 12(1), 2744. https://doi.org/10.1080/15388220.2012.731665CrossRefGoogle Scholar
VonHoltz, L. A. H., Frasso, R., Golinkoff, J. M., Lozano, A. J., Hanlon, A., & Dowshen, N. (2018). Internet and social media access among youth experiencing homelessness: Mixed-methods study. Journal of Medical Internet Research, 20(5), e184. https://doi.org/10.2196/jmir.9306CrossRefGoogle ScholarPubMed
Wang, C., Barlis, J., Do, K. A., et al. (2020). Barriers to mental health help seeking at school for Asian- and Latinx-American adolescents. School Mental Health, 12(1), 182194. https://doi.org/10.1007/s12310-019-09344-yCrossRefGoogle Scholar
Wargo, J. M. (2016). “Every selfie tells a story …”: LGBTQ youth lifestreams and new media narratives as connective identity texts. New Media & Society, 19(4), 560578. https://doi.org/10.1177/1461444815612447CrossRefGoogle Scholar
Wei, R., & Lo, V. H. (2006). Staying connected while on the move: Cell phone use and social connectedness. New Media & Society, 8(1), 5372. https://doi.org/10.1177/1461444806059870CrossRefGoogle Scholar
Wexler, L. (2009). The importance of identity, history, and culture in the wellbeing of Indigenous youth. The Journal of the History of Childhood and Youth, 2(2), 267276. https://doi.org/10.1353/hcy.0.0055CrossRefGoogle Scholar
Wexler, L. M., DiFluvio, G., & Burke, T. K. (2009). Resilience and marginalized youth: Making a case for personal and collective meaning-making as part of resilience research in public health. Social Science & Medicine, 69(4), 565570.CrossRefGoogle ScholarPubMed
Whitbeck, L. B., & Hoyt, D. R. (1999). Nowhere to grow: Homeless and runaway adolescents and their families. Aldine de Gruyter.Google Scholar
Williams, J., Bolland, K. A., Hooper, L., Church, W., Tomek, S., & Bolland, J. (2014). Say it loud: The Obama effect and racial/ethnic identification of adolescents. Journal of Human Behavior in the Social Environment, 24(7), 858868. https://doi.org/10.1080/10911359.2014.909343CrossRefGoogle Scholar
Williams, W. S., & Moody, A. L. (2019). Analyzed selfie: Stereotype enactment, projection, and identification among digitally native Black girls. Women & Therapy, 42(3–4), 366384. https://doi.org/10.1080/02703149.2019.1622901CrossRefGoogle Scholar
Ybarra, M. L., Mitchell, K. J., Palmer, N. A., & Reisner, S. L. (2015). Online social support as a buffer against online and offline peer and sexual victimization among U.S. LGBT and non-LGBT youth. Child Abuse & Neglect, 39, 123136. https://doi.org/10.1016/j.chiabu.2014.08.006CrossRefGoogle ScholarPubMed
Yip, T., Seaton, E. K., & Sellers, R. M. (2006). African American racial identity across the lifespan: Identity status, identity content, and depressive symptoms. Child Development, 77(5), 15041517. https://doi.org/10.1111/j.1467-8624.2006.00950.xCrossRefGoogle ScholarPubMed
Young, S. D., & Rice, E. (2011). Online social networking technologies, HIV knowledge, and sexual risk and testing behaviors among homeless youth. AIDS and Behavior, 15(2), 253260. https://doi.org/10.1007/s10461-010-9810-0CrossRefGoogle ScholarPubMed
Zhai, E., Jordan, K., Reeves-Miller, T., Xiao, T., & Charmaraman, L. (2020). Self-care and wellbeing on social media for adolescents of color. Panel presented at the Diversity Challenge, Boston College, Boston, MA.Google Scholar
Zhang, Y., & Leung, L. (2014). A review of social networking service (SNS) research in communication journals from 2006 to 2011. New Media & Society, 17(7), 10071024.CrossRefGoogle Scholar
Figure 0

Figure 3.1 The dual aspect of adolescent identity development and narrative and dialogical processes

Figure 1

Figure 5.1 Visualization of regions comprising the brain networks thought to be associated with digital media behaviors. Key control regions are shown for the fronto-parietal “executive” network, the cingulo-opercular control network, and the dorsal attentional network, including the frontal eye fields (FEF) and superior parietal lobule (SPL). Also shown are regions strongly implicated in reward processing and those thought to be connected to social processing in the brain.

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×