3.1 Twitter

Twitter – one of the most popular social networking or ‘micro-blogging’ sites – distinguishes itself as a source of textual data by accessibility and sheer volume. Almost all activity on Twitter is public by default, and its users simply broadcast their messages to whoever wants to listen. This openness yields a huge source of data – some ninety million tweets per day – that researchers have been quick to capitalize on.

Each tweet is a short 140 character message posted by an individual. Twitter’s Application Programming Interfaces (APIs) allow for real-time monitoring, so it is possible to track patterns over time with very low latency. Only a fraction of tweets (< 1%) contain geo-location data, but it is possible to (roughly) locate tweets using information from the poster’s profile. The patterns of following, replying to and otherwise engaging with accounts forms a social network of sorts that can be mined in addition to Twitter’s textual, temporal and geographical features.

Most studies were limited to Twitter’s free APIs, which deliver either a 1% random sample (known as the garden hose) or require specific vocabulary of terms and accounts to be monitored. Twitter’s terms and conditions prohibit redistribution, so there are no shared datasets and very little replication or direct comparison between the studies described below. While it is possible for researchers to share Twitter data without violating these conditions, there remains the question of whether it would be ethical to do so because of difficulties in anonymizing data Horvitz and Mulligan (Reference Horvitz and Mulligan2015).

Many studies of Twitter, such as Signorini, Segre and Polgreen (Reference Signorini, Segre and Polgreen2011) and Paul and Dredze (Reference Paul and Dredze2011) focus on physical health and disease, and are thus beyond the scope of the current discussion. Also, out of scope are the many other studies, which investigate feelings and emotions as they pertain to a specific subject (e.g. a product or a politician). This section focuses on investigations that address the mental health and emotional wellbeing of the people who tweet.

One might expect that tweets would provide limited insight given their brevity and public character. It is perhaps surprising then, that many researchers have successfully utilized Twitter data as a source of insights into the epidemiology of emotions (e.g how they propagate) and mental illness. For example, Golder and Macy (Reference Golder and Macy2011) used 509 million publicly available posts written by 2.4 million users from across the globe over a period of about two years. They showed that moods are more positive in the morning and decay during the day. They also found that people are happier on weekends and that seasonal moods change with day-length. These results hold few surprises we all know how much we love our weekends and crave sunlight but they do start to validate Twitter as a reliable signal of affective functioning.

Schwartz et al. (Reference Schwartz, Eichstaedt, Margaret, Kern, Dziurzynski, Park, Lakshmikanth, Jha, Seligman and Ungar2013) also validated the use of Twitter data in characterizing geographic variations in wellbeing, compared to traditional phone surveys about life satisfaction (Lawless and Lucas Reference Lawless and Lucas2011). In this experiment, about a billion tweets were gathered and, where possible, mapped onto counties in the United States. The paper does not describe exactly how many tweets were successfully geo-located, but the resulting dataset included 1,300 counties for which they found at least thirty twitter users who had each posted at least 1,000 words. The authors then compared the Twitter data against phone interviews and demographics data for the area (socioeconomic surveys and Census). Topic models created with the Twitter data improved the accuracy of life satisfaction predictions based on the demographic controls (county-by-county scores for age, sex, ethnicity, income, education) which were in turn more predictive than the prevalence of words from emotion lexicons. In combination, the three approaches achieved a Pearson correlation of 0.54 with the scores obtained through random direct surveys of people in a county, while the controls on their own achieve only 0.44 correlation. We Feel (Larsen et al. Reference Larsen, Boonstra, Batterham, O’Dea, Paris and Christensen2015) is a system that analyzes global and regional changes in emotional expression. The evaluation consisted of 2.73 × 109 emotional tweets collected over twelve-weeks, and automatically annotated for emotion (using Linguistic Inquiry and Word Count (LIWC)), geographic location and gender. The analysis confirmed regularities found in emotional expressions in diurnal and weekly cycles and these were reflected in the results for a PCA: The first component explained 87% of the variance and provided clear opposite loadings between positive and negative emotions. This study adds to the evidence that Twitter and possibly other publicly available social media data can provide insights into emotional wellbeing and illness across different populations.

De Choudhury et al. (Reference De Choudhury, Counts and Horvitz2013b) built a classifier for determining whether a Twitter post indicates depression. For the purposes of the current discussion, it is sufficient to say that the classifier produced a score that is higher for tweets that indicate depression than those that did not. The paper also describes a Social Media Depression Index (SMDI), which scores a group of tweets (e.g. those by a single author, or those from a city) by the prevalence of high-scoring tweets. One experiment in this paper is similar to the Schwartz et al. (Reference Schwartz, Eichstaedt, Margaret, Kern, Dziurzynski, Park, Lakshmikanth, Jha, Seligman and Ungar2013) study described above, but in this case the tweets were geo-located down to state level rather than county. The SMDI score was calculated for each state, and these scores achieved a 0.51 correlation against ground truth (defined as the data used as the training set) based on the Centre for Disease Control (CDC) calculations. It is important to note that this score is obtained entirely through analysis of Twitter; unlike (Schwartz et al. Reference Schwartz, Eichstaedt, Margaret, Kern, Dziurzynski, Park, Lakshmikanth, Jha, Seligman and Ungar2013) it does not make any use of background demographic data. In the same paper, the authors also calculated the SMDI scores for the twenty “unhappiest US cities” and achieved a strong positive correlation (0.64) with the prescription rates for common antidepressants.

Many other studies follow a similar pattern of quantifying topics and emotions over entire populations using Twitter. For example, Dodds et al. (Reference Dodds, Harris, Kloumann, Bliss and Danforth2011) introduced the Hedonometer, which cross-referenced a truly massive dataset of 4.6 billion tweets against their “Language assessment by Mechanical Turk” (LabMT) vocabulary of happy and sad terms. Like Golder and Macy (Reference Golder and Macy2011), their analysis of the resulting signal reveals unsurprising but reassuring patterns, such as increased happiness during the weekend. Mitchell et al. (Reference Mitchell, Frank, Harris, Dodds and Danforth2013) applied the Hedonometer using only tweets with exact GPS coordinates. At a state level, they found moderate correlations with incidences of gun violence (r = − 0.66), the American health index (r = 0.58), and the US Peace Index (r = 0.52). At a city level, they also found associations amongst Hedonometer levels and indexes of wealth and obesity.

However, the studies described above consider only the broad picture. Analysis at this level may be more forgiving, because mistakes can be averaged out as tweets are aggregated across entire states, counties and cities. For researchers, this poses the question: Are Twitter-derived measurements accurate enough to drill down to an individual?

One group from Microsoft Research has demonstrated two distinct approaches for conducting studies with individual Twitter users. In the first experiment, De Choudhury et al. (Reference De Choudhury, Gamon, Counts and Horvitz2013) developed a classifier that estimates the risk of a Major Depressive Disorder (MDD) before it happens. To gather a gold-standard dataset, they conducted a crowdsourcing task that required 1,583 volunteers to complete diagnostic surveys – specifically the Center for Epidemiologic Studies Depression Scale (CES-D) and the Beck Depression Inventory (BDI) – and answer questions about their basic demographics and history with depression. The same task invited (but did not require) participants to provide details of their twitter accounts. The task yielded two datasets; 305 twitter accounts with no indication of depression, and 171 accounts that scored highly on the CES-D survey and had also been diagnosed with depression at least twice in the last year (a requirement to qualify for MDD). A total of 2.1 million tweets (an average of 4,533 tweets for each account) were captured within the year prior to the crowdsourcing task. From this data, they identified behavioral features about engagement, emotion, language styles and medication used. These features were used to train the classifier, and distinguish between the depressed and non-depressed accounts. To summarize their findings, the depressed user accounts were

• • less likely to tweet or respond to others’ tweets,

• • more likely to tweet late at night,

• • more likely to use first-person pronouns (i.e. tweet about themselves),

• • less likely to use third-person pronouns (i.e. tweet about others),

• • less likely to follow others or gather followers.

In addition to these social and behavioral differences, the study also identified a lexicon of terms that were more common among the depressed accounts, such as mentions of medications, depressive symptoms and words related to disclosure. All of these signals were combined with basic demographic features (age, gender, income and education level) to develop a classifier that could distinguish between depressed and non-depressed accounts with a recall of 63% (the fraction of accounts that are automatically detected) and precision of 74% (the fraction that is labeled correctly).

In another study, the same authors focused on measuring behavioral and emotional changes that could be indicative of postpartum depression (De Choudhury et al. Reference De Choudhury, Counts and Horvitz2013a). Rather than surveying new mothers directly, they instead monitored Twitter automatically for birth announcements, using newspapers as a model. This identified 376 mothers after manual verification via crowdsourcing. The tweets of each mother were gathered for the three months prior to the birth notice and another three months after. In all, the dataset contained 37 k prepartum and 40 k postpartum tweets. These were analyzed as described above (De Choudhury et al. Reference De Choudhury, Gamon, Counts and Horvitz2013). Each mother was characterized with 33 features, and the ground truth (the training set) was based on a heuristic threshold of changes, rather than a direct questionnaire of depression. Instead the analysis identified mothers for whom twitter-derived measures of social engagement, linguistic style, etc. – the same signals that indicated depression in the previous study – changed dramatically between the pre and postpartum periods. It was assumed that these were mothers who were not adjusting well to parenthood, and were displaying signs of postpartum depression. A classifier was built that could from the prepartum data alone predict the mothers who would later exhibit these dramatic changes with an accuracy of 71%.

Coppersmith et al. (Reference Coppersmith, Dredze, Harman, Hollingshead and Mitchell2015) host a shared task that also attempts to make clinical diagnoses from Twitter data; in this case, for depression and posttraumatic stress disorder. Their work, and that of the task participants, is described in Section 4.4.

Twitter posts can be combined with geo-location information to produce approximate models of people’s emotion and context. For example, Sadilek et al. (Reference Sadilek, Homan, Lasecki, Silenzio and Kautz2013) used information from 6,237 users who shared their GPS location. They used linguistic features (i.e. based on the Linguistic Inquiry and Word Count - LIWC tool), behavioral (such as the number and time of tweets), social network (i.e. friends) and a summary quantity calculated from LIWC that categorized users into three states: positive, neutral and negative. By using this data, they found evidence of temporal mood patterns, demonstrated emotional contagion in groups and were able to predict when users were going to be in one of those three states (positive, neutral, negative) over the next ten days. Another study using the same dataset (Homan et al. Reference Homan, Johar, Liu, Lytle, Silenzio and Alm2014) selected 2,000 tweets around suicide risk factors (1,370 from the LIWC sad dimension and 630 with suicide specific terms) and annotated them. The annotations were performed by a novice and counseling psychologists and then judged by another novice. Each of the tweets was annotated as happy, no distress, low distress and high distress. The authors then trained a Support Vector Machine (SVM) to classify the tweets into the four categories and found F1-measures of 0.4–0.6 (the harmonic mean of precision and recall).

O’Dea et al. (Reference O’Dea, Wan, Batterham, Calear, Paris and Christensen2015) aim to separate out genuinely concerning tweets mentioning suicidal ideation from the large amount of flippancy and hyperbole that exists on the platform. They gathered a corpus of 2,000 tweets containing phrases like kill myself and tired of living and manually coded them as strongly concerning (14%), possibly concerning (56%) and safe to ignore (29%). Agreement between annotators ranged between 0.47 and 0.64 kappa, indicating that the task was strongly subjective. An automated SVM classifier was able to automatically separate strongly concerning tweets with a precision of 80% but a recall of only 53%.

3.2 Facebook

Facebook is another huge potential source of data. It is used by an estimated 1.13 billion people each day,Footnote ¹ and has become a major platform for promoting mental health campaigns and recruiting research participants to mental health studies. This section focuses on attempts to measure the emotional state of Facebook’s users.

Although Twitter users simply broadcast their messages to anyone who will listen, Facebook users exert more control to form closed audiences of friends and family members. This increased privacy may increase the user’s openness and honesty. Therefore, while Facebook data may be better suited to monitoring emotional state, it is more difficult to obtain by researchers outside Facebook.

Kramer (Reference Kramer2010) analyzed 400 million status updates from English speaking users within the United States, and developed a measure of gross national happiness by scoring each status update against the positive and negative terms within it (as given by the Affective Norm for English Words (ANEW) vocabulary described in Section 4.2). The measure was roughly validated in a similar fashion as Golder and Macy (Reference Golder and Macy2011), by visually inspecting the data for expected patterns like peaks during holiday periods and weekends and troughs during national disasters. A more rigorous validation was conducted by recruiting 1,300 Facebook users to complete a survey about life satisfaction. However, the happiness scores derived from their status updates achieved only a weak correlation with life satisfaction survey scores (r = 0.17). It is not clear whether this weak association was due to the simplicity of the algorithm (understandable, given the scale at which it was run), or to insufficient data (some of the Facebook users involved had as few as three status updates) or to factors beyond the scope of the study design.

Also relevant to this discussion is the study completed by Coviello et al. (Reference Coviello, Sohn, Kramer, Marlow, Franceschetti, Christakis and Fowler2014) which explored how negative emotion spreads as a contagion between Facebook accounts. The experiment investigated rain as a trigger for changes in valence, which was measured by the prevalence of known positive and negative terms within status updates (similar to Kramer Reference Kramer2010). The experiment demonstrated that not only do people post fewer positive and more negative posts during rainy days, but also that these posts have a statistically significant effect on the valence of their friend’s posts. Positive posts promoted positive posts and inhibited negative ones, and vice-versa; even when these friends were not experiencing the same weather patterns.

Another study of emotional contagion in Facebook showed how small changes in the filtering algorithm used in the Facebook Newsfeed can have a significant impact on the moods of users (Kramer et al. Reference Kramer, Guillory and Hancock2014). The experiment compared two filtering algorithms; one reducing the number of positive status updates a user was exposed to, and another suppressing negative updates. The valence of a post was again identified using a similar approach as in Kramer (Reference Kramer2010). In total, 690 k users were randomly selected and exposed to these algorithms. Approximately, 155 k participants within each condition posted at least one status update within the week. From these status updates, the authors measured a small (< 0.1%) but statistically significant shift toward positive terms (and away from negative ones) among those exposed to fewer negative posts. The opposite held true for the positivity-reduced condition. The study contributed interesting insights into how emotions propagate on social networks and the impact of design on people’s emotions, two underserved areas of research. But the study triggered an understandable flurry of outrage and concern for failing to inform or obtain consent from participants before attempting to manipulate their moods, as reflected in their Facebook updates (Calvo, Peters and D’Mello Reference Calvo, Peters and D’Mello2015).

Moreno and Jelenchick (Reference Moreno and Jelenchick2011) manually evaluated a year’s worth of status updates of 200 college students. Each update was coded against the DSM IV criteria for depression. The authors reported that 25% of profiles contained at least one depressive post, and five profiles had periods of multiple depressive posts each spanning several months; the authors considered these periods to match DSM criteria for a major depressive episode. This figure was expected, given 30% of college students report feeling depressed and unable to function each year (American College Health Association 2009).

The underrepresentation of depression terminology in the Facebook posts would seem to indicate that few people are comfortable sharing their depressive symptoms explicitly on Facebook. This does not invalidate Facebook data as a means of recruiting information that could be developed into a diagnostic tool, but it does point out the need to look for subtler cues. To our knowledge, the only published attempt to do so automatically is by De Choudhury and Counts (Reference De Choudhury and Counts2014), who recruited new mothers willing to share their Facebook data. In total, they gathered twenty-eight mothers who had been diagnosed with postnatal/postpartum depression (PPD) and 137 mothers with no experience of depression (a PHQ-9 Patient Health Questionnaire, was used to exclude mothers who displayed symptoms of depression but had not been clinically diagnosed with PPD). Their accounts were mined for activity for fifty weeks prior to and ten weeks after birth.

From this data, the authors observed that mothers suffering from PPD were

• • less likely to make posts or share media,

• • less likely to engage with or tag media left by others,

• • less likely to receive comments or likes from friends,

• • more irregular in the times that they interacted,

• • more likely to use first person pronouns (i.e. talked more about themselves),

• • more likely to ask questions.

Interestingly, the researchers did not find a statistically significant difference in the use of positive or negative emotion terms, as measured by LIWC. This is inconsistent with their previous work with Twitter (De Choudhury et al. Reference De Choudhury, Counts and Horvitz2013a), one explanation would suggest that Facebook posts are less emotionally expressive than tweets. Unfortunately, we cannot make any direct comparisons between Twitter and Facebook, because the authors did not develop or evaluate a binary classifier in the same way.

3.3 Blogs and journals

Blogs are used by many as personal diaries, and as a way to reflect and to share daily experiences. Increasingly, people can annotate their own posts with metadata (i.e. labels) about their emotions. Livejournal.com was one of the first to provide this self-annotation feature to users and it has been used in multiple studies. For example, Strapparava and Mihalcea (Reference Strapparava and Mihalcea2007) extracted a corpus of 8,671 LiveJournal posts labeled (by the post authors) with six emotions. The study sits slightly outside of the scope of this review, however, because they did not attempt to classify the blog posts themselves or the emotional state of their authors. Instead the posts were used as background training data to classify news headlines (from the SemEval 2007 dataset) and the emotion they were intended to provoke in the reader. Nevertheless, this work is described in more detail in Section 4.4.

Nguyen et al. (Reference Nguyen, Phung, Dao, Venkatesh and Berk2014) aim to automatically separate out LiveJournal posts that talk about tough times. For ground truth, they extracted a depression dataset of 38 k posts from sub-communities for depression, self-harm, bereavement, etc. and a control set of 230 k posts from other, less dire sub-communities. They experimented with a wide range of features, including LIWC (linguistic, social, affective, cognitive, perceptual, biological, relativity, personal concerns and spoken), affect (also based on LIWC) and topic models generated by LDA. They were able to automatically determine whether each individual blog post belonged to the depression or control set with an accuracy of 93%, and the best features were the LDA topics.

3.4 Other social media data

As new social media platforms are created they are used in mental health studies. For example, the nature of personal disclosure has been studied on Reddit (De Choudhury and De Reference De Choudhury and De2014), an online media platform similar to discussion forums but with an optional “throwaway” account type that improves anonymity. The results suggest that anonymity promotes disclosure, despite the often caustic nature of the discussions generated around a post. Although the number of throwaway accounts is proportionally small, the fact that 61% were used only once suggest these users do not want to leave any trail behind, but as the authors point out, this also means they cannot receive much social support.

Different countries often have different languages. They also vary on their socioeconomic variables, their conceptions of mental health, stigma and therefore support of those who are ill. Therefore, studying the social networks popular in different countries is important. Mixi, the most popular social network platform in Japan, which is the Organization for Economic Co-operation and Development (OECD) country with the highest suicide rate, was used to study the relationship between a person’s social network (i.e. relationships) and suicide ideation (Masuda et al. Reference Masuda, Kurahashi and Onari2013). According to the authors the effect of the age, gender and number of friends on suicide ideation was small.

Another interesting perspective is to look into peoples’ attitudes toward suicide (e.g stigma) as they can inform the type of suicide prevention interventions. A study using data from Weibo, a Chinese social network platform (Li et al. Reference Li, Huang, Hao, O’Dea, Christensen and Zhu2015) analyzed the social attitudes of people publicly commenting on others who made public their intention to commit suicide and found stigma was widespread and terms such as deceitful, pathetic and stupid were often used.

Those who suffer stigma maybe at risk of mental health issues and their communities might require special attention, for example, researchers have studied TrevorSpace, a social network popular amongst lesbian, gay, transgender and bisexual communities (Homan et al. Reference Homan, Lu, Tuurmcrochesteredu, Lytle, Lytle, Rochester, Silenzio and Silenzio2014a). The study did not use the text but only the connections amongst individuals and showed that some features are predictive of distress or mental-ill health.

3.5 Suicide notes

Suicide notes were first studied in the nineteenth century by Durkheim, a pioneer of suicide risk research (Durkheim Reference Durkheim1897). Shneidman created the field of suicidology around 1949 and collected and systematically studied suicide notes (Shneidman and Farberow Reference Shneidman and Farberow1957). In 1957, Shneidman and Farberow published a book that explored the reasons why people kill themselves, and collected genuine suicide notes paired with fake suicide notes written by a healthy control group that were demographically matched to the suicidal authors. The aim was to explore the difference between why people think others suicide, and why individuals actually do.

Pestian et al. (Reference Pestian, Nasrallah, Matykiewicz, Bennett and Leenaars2010) extracted from this book a corpus of thirty-three genuine (written by people who completed suicide) and thirty-three fake notes (written by people simulating a suicide note). They showed how difficult they are to distinguish manually: In their experiments, mental health professionals achieved an accuracy of 63%, whereas psychiatry trainees were only 49% accurate (i.e. worse than random chance). The authors were able to train a classifier that achieved 74% accuracy. However, the classifier relied somewhat on emotion annotations (e.g. guilt, hopeless, regret) that were made by a panel of three mental health professionals. The authors did not report the accuracy of the algorithm without these handcrafted annotations.

Removing this reliance on expert annotation appears to be the focus of Pestian et al. (Reference Pestian, Matykiewicz, Linn-Gust, South, Uzuner, Wiebe, Cohen, Hurdle and Brew2012), which presents a shared task for annotating suicide notes. Shared tasks are common computer science activities where research groups compete to solve the same problem. The task used a corpus of genuine notes written by people who took their own life, collected between 1950 and 2011. The notes were manually transcribed, anonymized and carefully reviewed, before being shared with a panel of 1,500 “vested volunteers” who were recruited from online communities and Facebook pages created to support grieving friends and family members. These volunteers were asked to annotate the notes for occurrences of negative sentiments (abuse, anger, blame, fear, guilt, hopelessness, sorrow), positive sentiments (forgiveness, happiness, peacefulness, hopefulness, love, pride, thankfulness) and neutral sentiments (instructions, information). In total, 900 suicide notes were each annotated separately by three volunteers.

In this shared task, 106 researchers in twenty-four teams tried to automatically reconstruct these manual annotations. They were given a training set of 600 annotated letters to develop their algorithm, which was evaluated on the remaining 300 letters (these were not released until algorithms were finalized).

There are two ways of calculating Precision, Recall and their harmonic mean F1 (a commonly used measure of classification accuracy). Macroaverages of these are the simple average over classes. Microaverages are obtained pooling per-document (i.e. post) decisions across classes, and computing P, R or F1 on the pooled contingency table. All of the top 10 competing teams achieved microaveraged F1-measures between 53% and 62%. An ensemble combining several of the systems could theoretically achieve an F1-measure of 76%. Section 4.4 describes the approaches of the top few teams. The F1-measure has its best value at 1 and worst score at 0. Microaveraged F1 was used to rank the teams.

This shared task used suicide notes written offline, mostly before the Internet era. But research on suicide notes written for the Internet have shown consistent findings with those written offline (Barak and Miron Reference Barak and Miron2005) and has been used as evidence for the development of support services (Barak Reference Barak2007).

Writing about suicide in the Internet age brings new challenges. Christensen et al. (Reference Christensen, Batterham, Mackinnon, Griffiths, Hehir, Kenardy, Gosling and Bennett2014) conducted a review of research studies that focused on three particular challenges: the use of online screening for suicide, the effectiveness of eHealth interventions aimed to manage suicidal thoughts and newer studies aimed to proactively intervene when individuals at risk of suicide are identified by their social media postings. The review highlighted the need for more evidence on the effectiveness of eHealth interventions for suicide prevention.

3.6 Online forums and support groups

Online peer-to-peer communities and support groups are one of the most promising ways of reaching out to more people, and allow them to discuss their health issues (Eysenbach et al. Reference Eysenbach, Powell, Englesakis, Rizo and Stern2004). These forums may be particularly good for reaching young people (O’Dea and Campbell Reference O’Dea and Campbell2010). Although the evidence on their efficacy is not yet strong, this is due in part to the difficulty of running systematic trials (Eysenbach et al. Reference Eysenbach, Powell, Englesakis, Rizo and Stern2004; Pistrang, Barker and Humphreys Reference Pistrang, Barker and Humphreys2008). One of the limitations of peer support style groups is that they need trained moderators who can help manage the community and can provide informed feedback when needed. When the communities grow this becomes a challenge.

The CLPsych 2016 shared task (Milne et al. Reference Milne, Pink, Hachey and Calvo2016) aimed to address such challenges of scale by allowing moderators to focus their efforts where it is most needed. It collected forum posts from ReachOut.com – a site for young Australians facing tough times – and asked participants to automatically triage them as green (no intervention required), amber (a moderator should ideally respond, but not urgently), red (a moderator should respond as soon as they can) or crisis (the post indicates someone is at risk of harm).

The task attracted sixty submissions from fifteen teams of researchers. Each team was initially given 947 annotated posts to develop and train their algorithms, and later given 280 posts – with annotations only seen by the task coordinators – for evaluation. The three best performing teams obtained a macroaveraged f-measure of 0.42, but used very different approaches to do so. Kim et al. (Reference Kim, Wang, Wan and Paris2016) combined relatively few features (unigrams and post embeddings) with an ensemble of SGD classifiers. Brew (Reference Brew2016) used traditional n-gram features with a well-tuned SVM classifier, and achieved the best separation of urgent posts (crisis and red versus amber and green) with 0.69 f-measure. Malmasi et al. (Reference Malmasi, Zampieri and Dras2016) gathered a larger number of features from not only the post itself, but also the preceding and following ones, and achieved the best separation of flagged posts (crisis, red and amber versus green) with 0.87 f-measure. The dataset is availableFootnote ² for researchers to continue developing their algorithms.

This triage task was a continuation of Moderator Assistant (Liu et al. Reference Liu, Calvo, Davenport and Hickie2013) a system that uses NLP to detect people in distress and helps moderators prioritize their workload (described in more detail in Section 5.5).

Other papers have analyzed patients’ self-narratives and provide further evidence of the potential of NLP techniques in PTSD diagnosis. For example, He, Veldkamp, Glas, and de Vries (Reference He, Veldkamp, Glas and de Vries2015) collected in an online survey in a forum for those seeking mental health aid and evaluated methods using n-grams and unigrams with Decision Trees, Naiïve Bayes, SVM and Product Score Model (PSM). Unigrams with PSM had the highest accuracy (0.82).

3.7 Summary

• • Most popular forms of social media have been used as data sources for mental health applications.

• • Number of users, language (i.e. English) and availability of APIs increase the chances of a platform being used. Twitter is the most widely used source of data mainly because the collection of public data is easy. Facebook is also common, often used by authors who also work for (or in partnership with) the company.

• • We only searched papers in English, and these mostly talked about content written in English. A few exceptions (e.g. Japanese) are mentioned. It would seem that NLP in non-English languages is an unexplored area. This may be related to the lower quality, or absence of NLP tools in languages other than English.

• • One of the most studied corpora of user-generated texts is a suicide notes collection because it was used in a shared task competition. Share tasks allow researchers from multiple disciplines to collaborate on a particular problem.

4.1 Gathering training data

Often the first step to build a classifier that can automatically detect emotions and mental health issues is to gather labeled data. This is a requirement for supervised machine learning algorithms that use this data for training and evaluation. The challenges of collecting and labeling data, particularly in realistic scenarios (i.e. “in the wild”) include time and cost (proportional to the amount of data being labeled), validity (particularly dependent on how the data was collected) and reliability (often requiring multiple trained annotators per document). These challenges have been discussed elsewhere, for example, in the literature on Affective Computing (Riva, Calvo and Lisetti Reference Riva, Calvo, Lisetti, Calvo, D’Mello, Gratch and Kappas2014). Semi-labeled and unlabeled data can also be used to train classifiers. For example, using Expectation-Maximization and Naive-Bayes classifiers (Nigam et al. Reference Nigam, McCallum, Thrun and Mitchell2000) contributed a technique that reduced classification errors by up to 30%.

4.2 Feature extraction

The literature would suggest that a combination of language and other forms of features is the most promising. This will likely be dependent on the application. This section describes these in more detail

Demographic features. Textual data can be complemented with socio-demographic data of individuals. Variables like county-by-county scores for age, sex, ethnicity, income, education and geo-location have been used in studies showing that these features can improve the accuracy of the classifier (Schwartz et al. Reference Schwartz, Eichstaedt, Margaret, Kern, Dziurzynski, Park, Lakshmikanth, Jha, Seligman and Ungar2013). These results are in line with evidence suggesting that social expressions of emotion are age and gender dependent, while location influences time and weather, which in turn, can also influence emotions.

Lexical features. Emotions are often inferred by the percentage of emotional terms in the posts. These are often computed using LIWC (Pennebaker et al. Reference Pennebaker, Boyd, Jordan and Blackburn2015; Tausczik and Pennebaker. Reference Tausczik and Pennebaker2010). LIWC is a software tool that provides psychologically-grounded lists of positive and negative emotional terms (amongst other categories reflecting writing style). The assumption, which is supported by research evidence (Strapparava and Mihalcea Reference Strapparava, Mihalcea, Calvo, D’Mello, Gratch and Kappas2014; Pennebaker Reference Pennebaker2011), is that emotion terms reflect emotion feelings (i.e. if you use more positive words, it is because you feel more positive). Language and feelings are indeed related, however, it appears that the relationship is weak. LIWC counts the number of words in a category (e.g. affective or social). The words are added to a category by the developers who often provide ‘validity judgments’ the correlations of judges ratings of the category with the LIWC variable. In particular, the correlation between the specific emotional words in LIWC and the emotional ratings by human ‘validity judges’ is a modest 0.41 for positive and 0.31 for negative terms; and these are amongst the lowest in the LIWC dictionaries (Pennebaker et al. Reference Pennebaker, Boyd, Jordan and Blackburn2015).

Emotion features from LIWC are often taken as good approximations of what people are feeling, thus, LIWC has been used in several mental health projects. But since the labeling is not done for clinical purposes the thesaurus does not directly indicate if, for example, a user wrote the word sad, has a higher probability of illness as would be measured by PHQ9 score or by the diagnosis of a clinician. Labeling of this type is generally done for a subset of each corpora or other information for the ground truth. For example, Kramer et al. (Reference Kramer, Guillory and Hancock2014)’s study that manipulated Facebook News Feed filters (described in Section 3.2) used emotion terms from LIWC. Other emotional thesauri include WordNet (Miller et al. Reference Miller, Beckwith, Fellbaum, Gross and Miller1990), particularly its extension WordNet-affect (Strapparava and Valitutti Reference Strapparava and Valitutti2004) and normative databases such as the Affective Norm for English Words (ANEW) (Bradley and Lang Reference Bradley and Lang1999). These thesauri can be used to group words, emotion words, for example, according to their valence or activation (i.e. arousal) (Calvo and Kim Reference Calvo and Kim2013).

Behavioral features. When people write they leave a trace of their behaviors. This metalinguistic information includes the time they wrote, for how long, if the writing was reply to another post, etc. The study by Sadilek et al. (Reference Sadilek, Homan, Lasecki, Silenzio and Kautz2013) using geo-location and twitter data (discussed in Section 3.1) is an example of how this type of data can be used in the modeling of moods.

Social features. These include the number of relationships (e.g. Facebook friends) and the topological map (i.e. who is related with who) that have been found to correlate to emotional and mental health constructs. The study by Sadilek et al. (Reference Sadilek, Homan, Lasecki, Silenzio and Kautz2013) used social features to model emotional contagion within groups. Others have shown how they can be used to improve the prediction of mental health states (Masuda et al. Reference Masuda, Kurahashi and Onari2013; Homan et al. Reference Homan, Johar, Liu, Lytle, Silenzio and Alm2014).

4.3 Feature selection techniques

Feature selection is required to reduce the number of features (i.e. the input space) used in the classification task. Without feature selection classification algorithms suffer from overgeneralization and are often less efficient (Witten and Frank Reference Witten and Frank2005). There are multiple ways of reducing the dimensionality of the problem including Principal Component Analysis, regression models and other statistical approaches. In text classification, Bi-normal separation (BNS) methods outperformed all other methods in an evaluation of twelve feature selection methods (e.g. Information Gain) over a benchmark of 229 text classification problems (Forman Reference Forman2003). The same study compared different techniques when only one dataset was available (the most frequent scenario) and showed that generally BNS outperformed other approaches. The exception was when precision was the goal in which case Information Gain yielded the best results.

4.4 Evaluating labeling systems

There are too many machine learning classification techniques to be discussed here, so we limit our discussion to those that have been used in mental health research. Complete descriptions of the algorithms can be found in machine learning textbooks or reviews (Sebastiani Reference Sebastiani2002; Strapparava and Mihalcea Reference Strapparava, Mihalcea, Calvo, D’Mello, Gratch and Kappas2014).

One of the most comprehensive ways to evaluate and compare classification systems through shared task competitions between independent research groups. This approach is used in the computer science and NLP communities to help identify the most accurate system. To make the process fair and reliable, the training data for the system is shared with the competing teams, but the test data is only made available to the shared task teams after the evaluation of the algorithms has been completed.

The suicide notes task described earlier is possibly the best example of shared task competitive challenges applied to mental health data. However, this approach has also been applied to related emotion detection tasks. For example, the SemEval-2007 task (Strapparava and Mihalcea Reference Strapparava and Mihalcea2007), where emotions were detected in a collection of news stories. Albeit not on mental health and not covered here, the SemEval-2007 and other similar shared tasks provide important insights for those working on mental health applications.

Micro and macro averages of precision, recall and F1-measures were calculated for each team. Microaverages weigh equally all data points so more common categories are weighted more heavily. In the suicide notes shared task, a team from the Open University in the United Kingdom had the best results with a microaveraged F1 = 0.61, the least performing team had F1 = 0.30, showing a large variation in the results when different techniques are used. The teams used different techniques including Part Of Speech (POS) taggers, thesaurus like WordNet (Strapparava and Valitutti Reference Strapparava and Valitutti2004), emotional lexicons and LIWC. The winning team’s submission (Yang et al. Reference Yang, Willis, De Roeck and Nuseibeh2012) is an example of the complexity of NLP systems. A text processing tool fed the notes to subsystems that identified instances of emotions, both at the token and the sentence levels. There were components to detect negations, emotion cues and terms. It also used numerous machine learning techniques that have shown to be successful in other applications, including Conditional Random Fields (CRF), SVM, Naiïve Bayes (NB) and Maximum Entropy (ME). The final output was the emotion labels at the sentence level.

The runner-up (Xu et al. Reference Xu, Wang, Liu, Tu, Sun, Tsujii and Chang2012) also used a combination of techniques. A key distinguishing factor in this submission was perhaps the augmentation of the dataset with semantically similar blog posts from LiveJournal and the SuicideProject (where bloggers can annotate data with emotions). They also used CRF and SVM algorithms in a binary fashion - one classifier for each label.

Since the other teams (Cherry, Mohammad and De Bruijn Reference Cherry, Mohammad and De Bruijn2012; Luyckx et al. Reference Luyckx, Vaassen, Peersman and Daelemans2012; McCart et al. Reference McCart, Finch, Jarman, Hickling, Lind, Richardson, Berndt and Luther2012; Spasić et al. Reference Spasić, Burnap, Greenwood and Arribas-Ayllon2012) reported details of their algorithms and outcomes, the shared task also provides insights into the techniques that did not produce satisfactory results. For example, Yu et al. (Reference Yu, Kübler, Herring, Hsu, Israel and Smiley2012) reported low accuracy using Wordnet, character n-grams and word n-grams (a contiguous sequence of n terms). They concluded that character n-grams and dependency pairs provide good features for emotion detection in suicide notes yet word n-grams and POS n-grams were less robust.

Another shared task was a 2015 Computational Linguistics and Clinical Psychology (CLPsych) shared task (Coppersmith et al. Reference Coppersmith, Dredze, Harman, Hollingshead and Mitchell2015). The data used for the task, and a hackathon held at John Hopkins University, consisted of anonymized Tweets written by 1,746 users who stated a diagnosis of depression or posttraumatic stress disorder (PTSD), with demographically-matched community controls. The shared task consisted of three binary classification experiments: (1) depression versus control, (2) PTSD versus control and (3) depression versus PTSD. The shared task had submissions from four research groups: University of Maryland who focused on topic models (Resnik et al. Reference Resnik, Armstrong, Claudino and Nguyen2015), University of Pennsylvania which used a variety of methods (Preoţiuc-Pietro et al. Reference Preoţiuc-Pietro, Sap, Schwartz and Ungar2015), University of Minnesota who used a rule based approach (Pedersen Reference Pedersen2015) and a submission by the workshop organizers and Microsoft that used character language models. Due to length limitations, we recommend reading those papers for further details.

A rarer evaluation approach involves studying a classification system while it is in use. Section 5.5.1 describes a triage system that uses the classifiers in real life. Their evaluation should in general go beyond the classifier accuracy and explore aspects such as how users perceived the triage system, and how they evaluate the possible usage scenarios, etc.

4.5 Summary

• • All feature selection and classification algorithms common in the NLP literature have been tried. Overall, we could not ascertain whether a particular technique was superior to other techniques.

• • LIWC is the most widely used tool for extracting features from text.

• • An understudied area is that of triage systems used in real time, with real users. Most studies are performed offline, where the diagnosis was not used to respond to actual users.

5.1 Web-based psycho-educational interventions

Psycho-education refers to the educational interventions where patients and their families can learn about the illness and the techniques and resources available to help them. Most examples of psycho-education interventions are fixed (i.e. not personalized) descriptions of symptoms and treatment written for a general audience. Although these have been excluded from this review, we explore here how they could be made adaptive and personalized using NLP. Exploring new approaches is important since research shows that web-based psycho-education interventions have therapeutic value and show positive outcomes (Ritterband et al. Reference Ritterband, Gonder-Frederick, Cox, Clifton, West and Borowitz2003). Internet-based interventions (CBT, psychoeducation and other) have shown to be effective in treating depression and anxiety (Spek et al. Reference Spek, Cuijpers, Nyklícek, Riper, Keyzer and Pop2007; Donker et al. Reference Donker, Griffiths, Cuijpers and Christensen2009), eating disorders (Neve et al. Reference Neve, Morgan, Jones and Collins2010), smoking and alcohol consumption (Bewick et al. Reference Bewick, Trusler, Barkham, Hill, Cahill and Mulhern2008; Myung et al. Reference Myung, McDonnell, Kazinets, Seo and Moskowitz2009) among other conditions.

Other application domains can provide ideas for innovative approaches in mental health. For example, in the same way that NLP has been used in educational technologies one could expect them to be used in psycho-education. Regrettably this has not been the case. A search on Google Scholar for “NLP psycho-education”, “computational linguistics psycho-education” did not return any positive results. NLP is used in education, for example, in automated assessment tools where users can answer questions and get formative feedback to improve their understanding. In health this could be done to improve their understanding of an illness. The content of websites could also be improved by measuring the complexity of the language used, allowing content editors to adapt the language to different age and socioeconomic groups.

A common marketing strategy is to personalize information with automatically generated text, for example, using simple templates to generate mailouts. Evaluations have shown that this type of semi-automatic and fully-automatic texts (i.e. letter, interventions) compare well with those generated by humans on a number of measures such as tone, rhythm and flow, repetition and terminology (Coch Reference Coch1996). Within health, personalization has been considered critical to patient-centered care and a number of studies have evaluated Natural language Generation (NLG) techniques in the authoring and personalization of webpages containing patient education materials. Unfortunately, the personalization of materials has been tried only in the context of physical health (e.g. surgical procedures, cancer, etc.). In the context of mental health, personalizing information (i.e. interventions) could also be helpful for family members, moderators of peer-support groups, the general public in social media, etc. Automated text generation methods could provide the moderators with information that they can use for quickly customizing and replying. This may even be useful for mental health clinicians, where information about a specific patient can be presented in the form of a report.

There are several possible tools used to build NLG systems including SimpleNLG (Gatt et al. Reference Gatt, Portet, Reiter, Hunter, Mahamood, Moncur and Sripada2009), a simple Java-based generation framework. The design of these tools is generally informed by the work of Reiter and Dale (Reference Reiter and Dale2000), who defined a general architecture used in most of the applications we reviewed. The architecture has three components connected together into a pipeline: (1) A Document Planer determines the content and structure of a document. (2) A Microplanner decides how to communicate the content and structure chosen by the Document Planer. This involves choosing words and syntactic structures. (3) Surface Realiser maps the abstract representations used by the Microplanner into an actual text.

One of the few examples of mental health applications is PyschoGen (Dockrey Reference Dockrey2007), an NLG-based system that changes its output based on the emotional state (set by the user). The experimental system followed the standard document planning → microplanning → realization pipeline. The goal of the project was not to send the text generated to real users or even generate highly natural output, but rather explore how emotional data could be generated. This type of approach could be suitable for mental health interventions that express empathy and compassion in line with the concept of client-centric health information and resources. Since there is little research on NLG applications to mental health, we describe here some related to physical health.

“Information Therapy (DiMarco et al. Reference DiMarco, Covvey, Cowan, DiCiccio, Hovy, Lipa and Mulholland2007) is a system providing preoperative information, that largely consists of personalizing resources that are normally distributed via brochures – each discussing a surgical procedure. The system was designed using a collection of reusable texts, each annotated with linguistic and formatting information, then the NLG tools automatically selected, assembled and revised the reader-appropriate pieces of text. This approach could easily be adapted to psycho-education interventions.

Similarly tailored information systems used in diabetes, migraine or cancer could be used to help those trying to learn about mental illness (though perhaps not for the patients themselves). Bental and colleagues (Bental, Cawsey and Jones Reference Bental, Cawsey and Jones1999; Bental and Cawsey Reference Bental and Cawsey2002) have described a variety of tailored patient education systems that may act as examples. For diabetes patients, PIGLIT provided personalized information about the disease, their hospital etc. (Binstead, Cawsey and Jones Reference Binstead, Cawsey and Jones1995). Similar work used hypertext pages for Migraine patients (Buchanan et al. Reference Buchanan, Moore, Forsythe, Carenini, Ohlsson and Banks1995), cancer patients (Jones et al. Reference Jones, Pearson, McGregor, Cawsey, Barrett, Craig, Atkinson, Gilmour and McEwen1999) and to accompany prescriptions (De Carolis et al. Reference De Carolis, de Rosis, Grasso, Rossiello, Berry and Gillie1996) have also been described. Tailored patient information systems have shown to be more effective than non-tailored computer system (or leaflets). For example, a Randomized Control Trial (RCT) of cancer patients (Jones et al. Reference Jones, Pearson, McGregor, Cawsey, Barrett, Craig, Atkinson, Gilmour and McEwen1999) (N = 525) with three conditions (paper brochures, personalized and general information hypertext) showed that more patients offered the personalized pages felt that they had learned more (and used it more), that the information was relevant to them, and even shared the information with others. Not all the results of NLG applications have been positive. A clinical trial generating personalized smoking cessation letters (Reiter, Robertson and Osman Reference Reiter, Robertson and Osman2003) after participants (N = 2,553) responded to a questionnaire indicated that those who received personalized information were no more likely to stop smoking than those who received a non-tailored letter.

5.2 Interactive, self-guided interventions

While content in tailored information systems is directed one-way – from the system to the user – similar techniques can be used to produce interactive psychoeducation interventions. This includes interactive software (websites, mobile apps, SMS) that offer an individualized step-by-step structure of information and guidance - a form of online self-help activity. The interactive system may provide a form of education as in the previous category, but may also only provide a structured set of activities for the user to complete. Back in the 1960s, a system called Eliza (Weizenbaum Reference Weizenbaum1966) was one of the first AI systems for this type of intervention. It emulated a Rogerian psychological perspective and used an early form of AI to engage the user in a dialogue of question and answer and asked empathic questions, based on the previous dialogue move. There has been some research around this model, where a software agent plays the role of the therapist (Bohannon Reference Bohannon2015).

Structured interventions such as Computerized Cognitive Behavior Therapy (CCBT) have received the most attention over the last twenty years. Although we could not find any CCBT interventions utilizing NLG, positive results from their application in related areas (Barak et al. Reference Barak, Hen, Boniel-Nissim and Shapira2008) make them a prime candidate for future research. For example, the efficacy of Moodgym (Christensen, Griffiths and Jorm Reference Christensen, Griffiths and Jorm2004), one of the pioneering tools for online CBT, showed that it was effective in reducing depression and dysfunctional thinking and in increasing understanding about CBT techniques. Since then many other CBT-based interventions have been developed and evaluated for the treatment of different mental health problems (Barak and Grohol Reference Barak and Grohol2011). Enough evidence for the effectiveness of online CBT has now been accumulated that in the UK computerized CBT is included in the public healthcare coverage (Kaltenthaler et al. Reference Kaltenthaler, Brazier, De Nigris, Tumur, Ferriter, Beverley, Parry, Rooney and Sutcliffe2006) and the market of commercial applications is booming (Aguilera and Muench Reference Aguilera and Muench2012). NLP techniques could possibly be used to personalize the activities or to automatically process personal diaries.

A limitation of current CBT interventions that could be addressed with NLG systems is that they can only help when the user proactively goes to the website or installs the app. They cannot use any of the information users generate during their everyday life, such as Facebook posts, tweets, emails, etc. The NLP techniques described in Section 4 can be used to gather some of this information from these sources, which could be used to make inferences and generate appropriate interventions to be delivered privately through the same social media.

5.3 Text messaging

High-tech options like relational agents and virtual reality environments are not always the most appropriate due to cost, reach or because they cannot be incorporated into everyday life. Other media, such as text messaging (SMS) can be used to send feedback using a mixture of preprogrammed parts and individually tailored information (Bauer et al. Reference Bauer, Percevic, Okon, Meermann and Kordy2003). For example, automated text messaging was used by Aguilera and Muñoz (Reference Aguilera and Muñoz2011) to support CBT in an adult, low-income population where smart phones and Internet access are not as common. It was aimed at increasing homework adherence, improving self-awareness and helping track patient progress by engaging patients through questions and simple activities. The small trial showed evidence that automated text messaging ‘conversations’ could be a low-cost approach to helping low-income populations.

Fathom (Dinakar et al. Reference Dinakar, Chaney, Lieberman and Blei2014) is a system to help counselors who provide help through SMS interventions (Crisis Text Line). Fathom uses topic models, a statistical modeling technique to track the evolution of themes in a conversation, in real time. The models were being built with 8,106 conversations held by 214 counselors. The evaluation did not yet include the topic maps with the full dataset but consisted of understanding the experiences of seven counselors using the system: they all found it most useful and had few feature requests.

A study to help patients with Bulimia Nervosa (Bauer et al. Reference Bauer, Percevic, Okon, Meermann and Kordy2003) used canned and personalized messages. In this study, patients would send a weekly message to the system and receive automated feedback that contained a template and personalized segments. The pilot program provided evidence that it was well received and could be useful in aftercare treatment of patients. Regrettably effectiveness was not reported.

Academic evaluations of commercial tools like Buddyapp were not found. Buddyapp involves writing a diary and engaging with a self-help CBT-type intervention by sending text messages, so these are not included here.

5.4 Online counseling

Technology in this category – often referred as e-therapy – is used to mediate the interactions between patients and clinicians (e.g. Email, Skype) (Grohol Reference Grohol, Kraus, Stricker and Speyer2004) and can replace a face-to-face session. The forms of communication or interaction can be more flexible for counseling and can be asynchronous (e.g. email, forums, etc.) or synchronous (e.g. chat, video conference, etc.).

Chatboxes are an interesting form of therapy. As shown by Dowling and Rickwood (Reference Dowling and Rickwood2013) in a systematic review of online counseling via chat they can be effective in helping patients. For example, clients using the online chat service have shown positive attitude toward counseling (Finn and Bruce Reference Finn and Bruce2008). We could not find any recent study using NLP-enhanced chatboxes, and regard this as an important area that requires further evaluation.

5.5 Online support groups and blogs

Online mental health support groups have become increasingly popular since the late 1990s. They enable people in distress to find others with similar needs and problems, to share feelings and information, receive support, provide advice and develop a sense of community. For example, the feedback received by peer-supporters on social media can help individuals reflect on their thoughts, which offers a type of mental health intervention through the feedback loop (Hoyt and Pasupathi Reference Hoyt and Pasupathi2008; Grohol Reference Grohol, Anthony, Nagel and Goss2010). Online peer-support groups are effective in mental health and the degree to which a user engages with the group through posting new messages or replying to others, has shown to relate to the quality of the health outcome (Barak, Boneh, and Dolev-Cohen Reference Barak, Boneh, Dolev-Cohen, Blachnio, Przepiorka and Rowiński2010). The review by Griffiths et al. (Reference Griffiths, Calear and Banfield2009) focused on measuring the effectiveness of online support groups. They reviewed 31 papers (involving twenty-eight trials) and found that 62.5% reported a positive effect on depressive symptoms, although only 20% used control groups. Regrettably the coding did not include any technical features of the studies, such as use of NLP.

Moderator Assistant (Liu et al. Reference Liu, Calvo, Davenport and Hickie2013), described earlier, provides trained moderators the ability to identify people at risk and to respond with resources (e.g. link to mental health related information). These moderators help keep the community together and raise the value of conversations. Besides the triage system described in Section 5.5.1, Moderator Assistant can also automatically create draft interventions that moderators are expected to edit using NLG techniques (Hussain et al. Reference Hussain, Calvo, Ellis, Li, Ospina-Pinillos, Davenport and Hickie2015). These drafts can improve the efficiency and quality of the feedback provided. For example, they can use therapeutic language and focus on issues that reflect the ethos of the organization. If the organization wants to focus on certain illnesses or treatments it can develop specialized content.

5.6 Summary

• • Text-driven psychological interventions are possibly the most common form of Internet intervention.

• • NLP techniques are rarely used as part of interventions.

• • Psycho-educational and self-guided interventions have many similarities to learning technologies where NLP have been widely used. There is an opportunity for one area to learn from experiences in the other.

• • Relational agents have been increasingly common, often in the form of embodied conversational agents or companions. The increased popularity of chatbots and automated messaging systems will possibly support growth in this line of research.

• • Text messaging, often considered as a low-tech option is very useful, particularly in certain populations.

• • Online counseling and peer support groups provide human-to-human communication where the computer mediation can add significant value through automatic triaging, summarization and personalization of the interventions.