11.3.1 Meeting at the Quantitative Middle Point

As mentioned in Chapter 4 on human swarm problem solving, many projects estimate a quantitative middle point as the best group contribution. The basic assumption is that a crowd can utilize more valuable information from a variety of sources compared with what a single individual can achieve. When this information is aggregated or combined in an effective way, “errors” or deviations from the optimal solution tend to cancel out if contributions are made independent of each other. If errors are randomly distributed and the group size is large, the law of large numbers ensures that the middle point provides an accurate answer (Surowiecki, Reference Surowiecki2005). Another example is a prediction market that use a market prize and individual betting to estimate the middle value of the crowd opinion (see Section 6.3). Deliberative Polling also illustrates that averaging can be used to aggregate crowd opinions in political decision-making (see Section 4.4).

An interesting paradox is that by maximizing a diversity of contributions, one increases the probability of aggregating the most accurate midpoint. For instance, one study that compared different crowdsourcing methods found that a crowd is wisest when it is maximally “diverse” in that its members are as negatively correlated with each other as possible (Davis-Stober et al., Reference Davis-Stober, Budescu, Dana and Broomell2014). When adding a new member to a group, the best strategy is to select somebody who is maximally different from others. By adding a much less skilled, but more diverse member to the group, the group became more accurate compared with just adding a new member with higher but similar skills. This follows the logic that if all individuals provide very similar predictions, there is little extra value in aggregation. Especially if the crowd is small, it is more beneficial to add a new member who can bring in information that is more diverse. When the members become less correlated with one another, the wisdom of the crowd effect becomes stronger. This is even more important than ensuring that the individuals make their estimations independently of each other. The intuition follows the same logic as predictor variables in a multiple regression analyses that are very similar to each other. Adding a new predictor, which gives new information, helps the model even if it is poorly correlated with the outcome variable. In the presence of some skilled members, it becomes more important to add members with truly different perspectives, which also helps to avoid biased opinions (Davis-Stober et al., Reference Davis-Stober, Budescu, Dana and Broomell2014). This finding supports the diversity prediction theorem, which underlines that crowd wisdom is maximized when judgements systematically differ as much as possible, not when judgements are independent and cancel each other out (L. Hong & Page, Reference Hong and Page2004).

11.3.2 Finding a Balanced Representation of All Sides

If we look at the content that gets attention today in the online setting, it is sensational rather than balanced content. According to Shifman (Reference Shifman2014: 66–71), viral content is designed in a specific way to become popular. First, this type of content will often be funny, and it can be surprising, interesting, or practically useful. People tend to share content that arouses them emotionally. Positive stories can generate a feeling of being in contact with something greater than oneself, like natural wonders or people overcoming adversities. However, stories that evoke negative feelings of anger and anxiety can also become viral. The disadvantage with this trend is that rational arguments are left out because feelings are considered more important in making the content viral. Second, clear and simple news stories spread better than do complex ones. Jokes are more “sharable” because people can understand them quickly. Since the problem is simple, the solution offered is equally straightforward and easy to digest. However, the risk is that complex problems are oversimplified to increase the likelihood of making it viral. It is not necessarily the high-quality content that becomes most popular.

Still, there are some interesting examples of knowledge products that one could claim resemble a “golden middle way” in an attempt to include “all perspectives.” For instance, Wikipedia articles show that it is possible to successfully synthesize content into coherent articles with competing or opposing views. One explanation may be the shared consensus norms like the “neutral point of view” policy that explains that contributors are not supposed to write an “objective” text, but instead seek a fair representation of all sides. The focus on using reliable secondary sources transcends the debate from being a question of whether or not to include specific content to asking how content should be included and with what sources (Algan et al., Reference Algan, Benkler, Morell and Hergueux2013).

Likewise, in argument maps that promote deliberation, it is important that different political views are presented in a fair way. Argument maps can perhaps complement wiki technology, which is not able to capture knowledge about contentious topics in an efficient way. In simple decision-making, people will perhaps automatically “meet in the middle.” However, in complex decision-making where there is no simple correct answer, it may be more important to get an overview of the issues that divide people most deeply, or identify the best ideas for one specific issue and try to understand why it is like that. In a deliberative community, the goal will not only be to maximize the collective outcome, but also avoid that the minority feels alienated. This can undermine the decision and the future cohesion of the community itself. If discussions are to be experienced as fair and legitimate, the argument mapping tools must be designed to minimize regret rather than solely maximizing the majority outcomes (Klein, Reference Klein2017). Still, one can question whether the organization of arguments maps into pros and cons risks polarizing the debate even when members have better access to all arguments.

11.3.3 Identifying Commonalities

Another interesting consensus-making approach is vTaiwan; it seeks to find the “golden middle way” by emphasizing consensual statements in the crowd. vTaiwan was established in the aftermaths of the Sunflower movement, a sudden three-week demonstration in 2014 by Taiwanese protesters who occupied parliament because of a trade bill that would bring their country closer to China. The protesters eventually backed down, but it raised another bigger question: how could Taiwan’s government listen better? To find a solution, Taiwan went to the civic hackers who had been part of the protest and asked for help. They wanted to avoid something like this from happening again (C. Miller, Reference Miller2020).

These civic hackers were organized in leaderless collective called g0v (pronounced “gov zero.”). They believed in radical transparency, building on the values from the open source software philosophy, by which everyone should be included in the decision-making process. Not only were they invited to give advice, but one of its members, Audrey Tang, was appointed the country’s digital minister. A new group with a very different worldview was given political power in a way we have not seen before. The civic hackers saw the main problem to be a lack of communication and information between the citizens and the government. Elections were too infrequent to give government enough information about what the public wanted, while referenda and debates split society. Instead of measuring division, the group thought one should instead design systems that could improve the communication flow and invent new ways of constructing consensus (C. Miller, Reference Miller2020).

Although the Internet could offer a solution, it was also part of the problem in Taiwan. Online politics in Taiwan was polarized and primarily made people angrier. There were no platforms that let citizens express their preferences to the government in a constructive way. Like in the rest of the world, social media like Twitter, Facebook, and YouTube had turned the political debate into a game focused on capturing attention. The algorithms prioritize information that gets the most clicks, independent of how crazy it is. These platforms are engineered to keep you on the site, leading to content that provokes the strongest emotions. This amplified the politics of division and outrage rather than nuanced discussions or attempts to compromise (C. Miller, Reference Miller2019, Reference Miller2020).

In an attempt to create a new type of democratic process that pulled people together rather than split them apart, the civic hackers invented vTaiwan (the “v” stands for virtual). The environment let citizens, politicians, and others discuss proposed laws in addition to joining face-to-face meetings and hackathons. The political goal is to help policymakers strengthen the legitimacy of their decisions by not only letting citizen vote on questions posed by the government, but also by letting them pose the questions. If it succeeds, it is expected to produce something that the government can turn into new laws (Horton, Reference Horton2018; C. Miller, Reference Miller2019).

vTaiwan builds on open source tools, and one of the key parts is the Pol.is platform. The platform lets anyone share their feelings openly with each other, and it is possible to agree and disagree with others. It is also possible to upvote or downvote other people’s comments. In this way, Pol.is resembles any other online forum. However, there are also some major differences, as many of the features are designed to bring the groups closer together. When the debate begins, Pol.is draws a map of the debate and shows everyone where they are positioned and where all the different knots of agreement and dissent are positioned. The upvotes and downvotes generate an opinion map of all the participants in the debate, clustering together people who have voted similarly. Even when there are hundreds or thousands of comments, it will be easy to identify like-minded groups rapidly in the map, and get an overview of where there are divides and consensus. The comment system is interesting because it can include a large population of several hundred persons and still stay coherent (Horton, Reference Horton2018; C. Miller, Reference Miller2019, Reference Miller2020).

However, more importantly, the platform does not highlight the most divisive statements, but instead gives most visibility to the most consensual statements. Attention is given to the individual suggestions that find support across the different subgroups and not only in one cluster. After viewing the map, people will usually begin to draft comments they think can win votes from both sides of a divide, gradually bringing subgroups closer together. This specific design feature motivates a competition, bringing up the most nuanced statements that can win the vote of individuals across subgroups. Therefore, most of the participants will typically spend far more time discovering their commonalities rather than just discussing one particular sub-issue. Because of the visualization feature, people can also easily follow the crowd opinion as it unfolds. As such, the technological design in the platform builds on a conception of consensus rule and not majority rule (Horton, Reference Horton2018; C. Miller, Reference Miller2019).

Furthermore, because it is not possible to reply to comments, people lose interest in making divisive statements. This almost completely removes the problem of misbehavior. There are also fewer problems with redundancy in this method since only some statements will receive attention in the platform, and it is not necessary to include all statements like in an argument map (Horton, Reference Horton2018; C. Miller, Reference Miller2019).

Within a period of three to four weeks, most people will usually agree on most of the statements. This differs from politics when people often spend most of their time discussing their disagreements. By gamifying consensus, the platform is able to create a new type of unity in the process (C. Miller, Reference Miller2020).

When people express their views, the online platform gives most visibility to those finding consensus across different subgroups. Groups become more aware of what they can agree upon, their hidden consensus. This is different from traditional social media, where algorithms often give primary attention to divisive statements or provocations that receive many comments (C. Miller, Reference Miller2020). The case reports from Taiwan show that within a period of three to four weeks, most people will agree on most of the statements. While there might be half a dozen polarizing statements, there may be 20 or 30 statements that create broad unity. The success can be attributed to the fact that these commonalities are made more visible than the disagreements. The technology allows people to converge and form a polity. This is not done by resolving bitter disagreements in an online setting, but by pointing to a way forward by revealing the numerous areas most people can agree upon (C. Miller, Reference Miller2019).

In one of the platform’s early successes, the political issue was how to regulate the company Uber, who were in conflict with the local taxi drivers. Within a few days, the platform voters had moved into two large groups, one pro-Uber and one anti-Uber. The online debate covered anything from calls to ban Uber or let the free market decide. Then something surprising happened when both groups were still trying to attract more supporters. Some members began posting statements that everyone could agree were important, such as rider safety and liability insurance. Gradually, these recommendations were refined as individuals tried to get more votes. Eventually, almost everyone had come up with seven recommendations they could agree upon, for example, that private passenger vehicles should be registered. Underneath an angry debate about Uber regulation, everyone realized that they just really cared about safety. The conflict between pro- and anti-Uber camps had been transformed into a consensus that described how they could both exist, but on specific terms. After the online deliberation, the recommendations were discussed in a face-to-face setting with Uber, the taxi drivers, and experts. The different stakeholders had already been drawn closer to each other as the online debate had identified several “consensus items” – statements that most people agreed with. The government followed the recommendations from vTaiwan and let Uber operate, but only with licensed drivers (Horton, Reference Horton2018; C. Miller, Reference Miller2020).

Another interesting example is a conflict over whether drunk drivers should be beaten with canes. More than 10,000 voted on a recent proposal that advocated caning as a punishment for drunk driving and sexual assault, but there was also fierce resistance against this kind of punishment. The government challenged vTaiwan to find consensus where none seemed to exist, with groups both supported and rejected caning with emotional intensity. Initially, opinion was divided into three camps: one group each for and against caning, and a third group argued that the punishment should be more serve. Surprisingly, as in the earlier example, the crowd in Pol.is transformed the discussion. The consensus opinion that emerged had nothing to do with caning, but focused on political strategies preventing such crimes. The crowd had found out that “To cane or not to cane?” was the wrong question to ask. Instead, the group began proposing legislation including alcohol locks and confiscating drunk drivers’ cars. This solution would not have emerged from a traditional online petition that only gave people the option of voting yes or no (Horton, Reference Horton2018; C. Miller, Reference Miller2019).

Still, there are several significant challenges in this new type of e-democracy. vTaiwan has mostly focused on digital issues and not yet on a national issue with entrenched polarization. Civil society needs to learn how to use such tools in cooperation with the government. A large part of the population is not comfortable with using such tools. Only 200,000 people have participated in the vTaiwan discussion. However, nearly five million of the country’s 23 million inhabitants have participated on the new platform Join, which builds on a similar method that attempts to create a new public service culture (Horton, Reference Horton2018). It is problematic to implement such systems on a wider scale when there is a group of people who do not use this type of technology. Young people are usually more tech-savvy, even though all age groups are increasingly using social media. One will perhaps need to use lottocratic methods to ensure demographic representation on important issues.

Another challenge is to incorporate these decision-making structures such that they become a permanent part of the government. When Taiwan’s finance ministry decided to legalize online sales of alcohol, there was concern that online sales would make it easy for children to buy liquor. Alcohol merchants and social groups were just talking past each other, and in 2016, 450 citizens joined vTaiwan to deliberate on the issue. In just a few weeks, both sides discovered that they were actually willing to give the opposing side what it wanted, and they were able to formulate a set of recommendations together. Sales would be limited to a few e-commerce platforms and distributors, and purchases would be collected at convenience stores, making it very hard for children to collect them without arousing suspicion. One month later, the government incorporated the suggestions into a draft bill that it sent to parliament. However, because of a change in administration, the online alcohol sales bill was never implemented, showing the risk of “openwashing” – that such processes can end up only creating the pretense of transparency. Because the government can ignore the discussions, vTaiwan may eventually end up as a tiger without teeth. Many participatory governance projects around the world suffer from the same problem, thus making it difficult to gain credibility with citizens (Horton, Reference Horton2018).

Public officials and politicians also need to regard online comments as something other than protests. They need to acknowledge the potential in mobilizing citizen expertise (Horton, Reference Horton2018). Moreover, the experimentation continues. All government drafts of law are now subject to 60 days of public commentary that will be organized in a similar way as in vTaiwan. However, the survival of the platform still depends on the power of the ruling party (C. Miller, Reference Miller2019).

The most important result of vTaiwan is that it shows that online deliberation is possible if the technology is designed in the right way. Mainstream social media have largely failed in creating a real political debate because they amplify polarizing content. In contrast, consensus platforms can include both citizens and politicians in more constructive ways. By clearing away the divisiveness, systems like vTaiwan can help the crowd agree and give advice to the government in making laws and regulations (C. Miller, Reference Miller2019, Reference Miller2020). In the political domain, it appears that intelligent contributions should highlight consensus elements. The system’s potential to reconnect people who are in conflict with each other provides evidence of a promising new approach that should be further examined in the future.

11.5.1 A Modularization Strategy

A key challenge in many CI projects is how to organize and combine a large number of contributions in an effective way. In general, a modularization strategy is the most common way of organizing the collective work. The complete work is split into many small subtasks that can be performed independently of each other. Collective work on open source software is a classic example, which make it possible to do separate subtasks that still depend on each other. It is easier for participants to organize their attention around single issues that can be separated from each other. It is not even necessary to understand the whole project. Individual work can be done separately from all the other ongoing work, making it much easier to contribute. It may take a lot of extra time to get an overview of all the content or the complete discussion. Another major advantage with modularization is that it builds on the principle that any contributions matters, even very small ones (Nielsen, Reference Nielsen2011).

Many citizen science projects that involve analysis of huge amounts of data build on modularization. They are designed as simple well-structured tasks of low complexity (e.g., Galaxy Open Zoo). The problem is clearly defined in advance, it can easily be split into small task pieces, and the criteria for evaluating contributions are well understood (Franzoni & Sauermann, Reference Franzoni and Sauermann2014; G. Graham et al., Reference Graham, Cox, Simmons, Lintott, Masters, Greenhill and Holmes2015). In one classical example from 2002, NASA published photos of the surface of Mars on an open website so anyone could volunteer to help mark and classify craters. During the first six months, 85,000 users visited the site. In total, volunteers made 1.9 million entries during the project period. Normally, this type of work would require many months of work by a scientist or graduate student. The maps of Mars were divided into many small segments with a simple marking tool. This made it possible to modularize the tasks and split them into smaller components (or modules), which could be worked on independently before they afterwards were put together again. One microtask usually only required a couple of minutes’ work. The users could therefore choose to either quickly mark one crater or work for hours with many craters. One study found that a small group of clickworkers did most of the work, while one-time contributors did 37 percent of the total amount of work. People contributed for the fun of it. Because the modules were independent of each other, contributors could choose when they wanted to contribute. This strengthened both user autonomy and flexibility in the project. The quality of the work was also high because the tasks were discrete and highly modularized. By averaging the coordinates of the user contributions, the results were assessed to be at approximately the same level as an expert scientist (Benkler, Reference Benkler2006; Malone et al., Reference Malone, Laubacher and Dellarocas2009). This example illustrates the potential in letting the crowd solve simple problems that have one correct solution. Benkler (Reference Benkler2006) claims that the number of people who can participate in a project is inversely related to the size of the smallest-scale contribution necessary to produce a usable module. If the granularity of the modules is small and the required work effort is sufficiently low, there will be less need for extra “incentives” because individuals can more easily do it in their leisure time.

Another prominent example of modularization are the millions of articles in Wikipedia. In every article, contributions of any size matter. Thousands of persons will be working on thousands of separate articles in Wikipedia, but they do not need to know of each other. Still, articles will be linked together in the online environment. The size of the encyclopedia is much larger than printed encyclopedias, demonstrating how the complexity of solutions can scale in the global online setting. According to Nielsen (Reference Nielsen2011: 33,56), the ideal is to create a technological platform architecture that gives every participant an easy overview of how they can make the best contributions. This can broaden the range of expertise that can be used, making it easier for newcomers to join the project (reduce barriers to entry) and reduce the time needed to perform a task (Nielsen, Reference Nielsen2011).

On the other hand, if projects become too monolithic, it is more difficult to get an overview of what is going on. This is why the Linux project put much effort into modularizing the collective work related to the development of an open source operating system. Many attempts to produce complex knowledge products have also failed because of insufficient modularization (Crowston et al., 2018). For example, although open textbooks can easily be reused and adapted, it has been very difficult to let a large group of people co-produce a textbook like in the Wikibooks project. Although the “big brother” Wikipedia is an enormous success, high-quality textbooks need to be both larger and more coherent, making the work more difficult for amateurs. Nor is it possible to have different writing styles, and one needs to follow local or national curriculum standards if the textbook is to become a part of the syllabus in colleges or schools. These guideline requirements have constrained the project’s granularity, making it more difficult for outsiders to contribute (Benkler, Reference Benkler2006).

In ill-structured tasks, the specific subtasks are not obvious, contributions cannot be easily evaluated, and the problem space will first emerge during the work. When contributions build on each other and are highly interconnected, it is much more difficult to modularize the tasks (Franzoni & Sauermann, Reference Franzoni and Sauermann2014). For example, the Galaxy Zoo Quench project illustrates how difficult it is to involve amateurs in writing a research paper together. One reason is that the different parts of a paper, like the introduction, the review, and the data analysis need to be consistent with each other. They cannot be treated as separate modules. The production of a coherent paper requires additional work in planning the writing process and revising the parts so they fit with each other. Only a few tasks in writing, such as proofreading, can be compared with galaxy classification that is done without affecting other tasks. The voice and writing style of the different sections needs to be similar. In addition, problems at a conceptual level are more difficult to identify and resolve. The project failed because it was unable to decompose the analysis into specific subtasks (Crowston et al., Reference Crowston, Mitchell and Østerlund2018). One alternative strategy could perhaps have been to enforce stronger centralized control with a coordinator who organized and modularized the work in advance.

Attempts to let a large group of people write a novel together have also failed. One example is the Million Penguins project, which recruited 1,500 persons to write a novel together with wiki software. This project never became a success because people were not able to work effectively together. A major difference compared with writing an encyclopedia is that it is very difficult to modularize a novel. Every sentence in a novel is to a much larger degree connected with all the other sentences in the overall story. Different parts in the book will be connected with each other in intricate ways. Modularization may lead to dissonance and incoherence. Nor is the wiki software ideal at keeping an overview of longer pieces of writing and the relationships between them. On the contrary, it is a tool that is designed to work well for the collaborative writing of short, independent articles in a reference work (Nielsen, Reference Nielsen2011: 53–54).

11.5.2 Modularization in Strongly Interconnected Content Structures

Depending on the complexity of solutions, the modularized task will either be part of a strongly interconnected content structure or a loosely connected content structure. Examples of strongly interconnected content structures are open database projects (e.g., Bird, OpenStreetMap) or collective argument maps. When the structure is predefined, contributor guidelines will usually be more precise. In open databases, contributors are given specific instructions on the type of information that is required. One recent example is participatory mapping in sustainability projects (Nicolosi, French, & Medina, Reference Nicolosi, French and Medina2020). In these modularized structures, each separate module is built from scratch, they will at the same time be part of a large collective knowledge construction, building on the others’ work. The goal is to produce a richness and diversity that is still easily accessible for others. In addition, it will be relatively easy to get an overview of the complete collective work in these structures.

In eBird, every uploaded bird observation is a module that becomes part of a large database with all observed bird activities. In comparison with an argument, a bird database will not to the same degree depend on filling the missing gaps because users can upload their information independently of others. These databases are collections of information, where individuals can upload information separately from each other. A large amount of data is effectively aggregated because there is no need to coordinate information between the contributors. However, the missing data spots on the map are visible and provide volunteers information about what areas need to be further explored. In this way, new contributions can also build on previous contributions in these databases. One example is the Gulf Coast Oil Spill Tracker, a data visualization tool within eBird. It was a mash-up of several datasets that included the locations of the ten bird species of conservation concern and the current forecast of the oil slick. It provided a valuable source of timely information. In the year following the disaster, more than 4,000 birdwatchers along the gulf submitted over 110,000 checklists. These data were used to estimate the number of birds that died but were never found. It illustrates that baseline monitoring from leisure birdwatchers can be valuable in many different ways (C. Cooper, Reference Cooper2016: 46–47).

An argument map is another example of a tightly interconnected content structure. Each argument will typically represent a module, and it will either be part of a number of pro-arguments or counter-arguments. These arguments are organized in a hierarchical structure. New contributors will need to build on others’ work by positioning a new argument within the framework of arguments that have already been published. The credibility of an argument depends on how it fits into a coherent map structure.

One challenge today is that most groups are better at producing more arguments that can reinforce their prior beliefs compared with finding counter-arguments. In the argument map, exposure to counter-arguments is assumed to lead to a possible change of confirmation bias. “Confirmation bias” is the empirically well-established tendency of individuals to seek out arguments that support a position they already hold. People tend to give more weight to the most striking pieces of information or simply to those pieces of information they already possess, instead of looking for relevant information that might be lacking. When group members disagree, they are most likely to find arguments for their own position, but this can be beneficial in an argument map as it provides more detail to specific arguments. Because people are usually competent at falsifying statements that oppose their views, this can be a useful skill that can be utilized in the map. If both parties participate in this process, it may contribute to the development of a more complete map with more in-depths explanations. However, if the argumentation map is skewed in one specific direction, there is a risk that deliberation can strengthen group polarization. Close attention must be paid to the how decisions are achieved when groups strongly disagree (Landemore, Reference Landemore2013).

11.5.3 Modularization in Loosely Connected Content Structures

Furthermore, some CI practices center on loosely connected content structures. Many knowledge products today, whether it is a text or a video, are published openly and become accessible for others. The work becomes a tiny module in an enormous network of interlinked information on the Internet. From this perspective, every video on YouTube is a separate module in a platform where the most realistic way of finding the video is by using its search engine.

However, one of the main challenges in the new global online environments is how to tackle the problem of information overload. While the traditional expert model built on the transmission of knowledge from a formal expert to many less knowledgeable others, the networked online environment illustrates how anyone with some skill or knowledge can share their knowledge openly with others. The problem is that both the number of experts available and the number of amateur contributions have become enormously large. Searching through hundreds of scientific papers that describe an issue is far too time-consuming for a single individual. While it is still possible to synthesize content in text documents, this is much harder to do with videos because of its multimodal properties. The most relevant videos will therefore need to be identified, and viewing many videos to select the most relevant is far too time consuming for an ordinary individual.

Today, search engines are considered the best option to solve this challenge of finding modularized information in a loosely connected content structure. These search engines require that citizens find and assess relevant information on their own. Although search engines like Google contain links to a large proportion of the web, only a tiny percentage is made easily accessible to users. Unlike television or even printing, it is easy for anyone to publish their opinion to a large, potentially global, audience, but this does not imply that anyone will pay attention to this “diversity of content.” The assumption that computer networks are more democratic, and necessarily provide a greater voice to everyone, is misguided (Halavais, Reference Greenwood2018: 101).

While the distributed nature of computer networking makes it less likely that a small number of interests can control it, this does not imply that the web is a level, uniform network. Most likely, any given contribution on the web will be lost in the flood of similar efforts. While search engines make it easier to find answers to specific questions, this is done at the expense of the larger, diverse world of information and opinions. Of the millions of blogs in the blogosphere or videos on YouTube, most get viewed by only by one or two people, while a small number get millions of hits, this being far from equal access to the greater web audience (Halavais, Reference Greenwood2018: 101).

Paradoxically, search engines that can retrieve enormous amounts of information are today being criticized because they oversimplify the available information when they only display a limited number of hits. A search for “staph infections” will generate a hit of about 1,200,000 pages, as health topics are popular on the Internet. The first three results, which most users will check, are from mainstream, relatively well-respected sites, but in total only 234 results are displayed. It shows that an enormous number of pages are left out. When most people use a search engine like Google, they typically only check the top ranked pages. On one side, it is necessary to select only a few results because our attention span is limited. The goal of using the search engine is to avoid the “junk” on the web and provide higher precision in the search results of the search engine. However, the hyperlinked structure tends to send the searches along the same path to the same informational sources. The result is that general-purpose search engines overrepresent the central tendency and reduce the diversity of the information when they operate in the hyperlinked structure of the web. Even though the search tools are not intentionally designed to amplify a few top selections, the ranking systems are conservative and reinforce existing orders of authority. It is not a question of whose power they conserve, but rather that they tend to enforce a “winner-take-all” structure that is difficult to break free from. The network structure on the Internet is organized in such a way that a lot of attention is given to a few sites, while many sites receive no attention at all. The risk is that search engines amplify a global groupthink monoculture and makes it more difficult to find local cultures and practices. There is a naturally tendency to move towards monopoly: “one search to rule them all.” This becomes a problem when large search engines are used by a very diverse set of users with different needs. It becomes more difficult to serve the interests of marginalized groups because a general relevance in search engine rankings does not necessarily match an individual query situated within a very particular aim (Halavais, Reference Greenwood2018).

In the network, only a few links survive and are amplified, leading to concentrations of power and influence. This results in a fight for attention in the network structure. Virality itself is also highly persuasive; view counts inform viewers that many others find a message interesting, and this amplifies the spread of the message. If the author is already famous, it is more likely that people will share the work. In viral marketing, there are two types of preferred influencers – the “hubs,” people with a high number of connections to others, and “bridges,” people who connect between otherwise unconnected parts of the network. In marketing, it is much more important to get the attention of these highly connected individuals compared to just sending the message to “regular” users (Shifman, Reference Shifman2014).