Tagged: information

Reading list from ICWSM17

In one of the workshops of the first conference day, ”Studying User Perceptions and Experiences with Algorithms”, the participants recommended papers to each other. Here are, if not all, then most of them, along with their abstracts.

Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130–1132.

Exposure to news, opinion, and civic information increasingly occurs through social media. How do these online networks influence exposure to perspectives that cut across ideological lines? Using deidentified data, we examined how 10.1 million U.S. Facebook users interact with socially shared news. We directly measured ideological homophily in friend networks and examined the extent to which heterogeneous friends could potentially expose individuals to cross-cutting content. We then quantified the extent to which individuals encounter comparatively more or less diverse content while interacting via Facebook’s algorithmically ranked News Feed and further studied users’ choices to click through to ideologically discordant content. Compared with algorithmic ranking, individuals’ choices played a stronger role in limiting exposure to cross-cutting content.

Bucher, T. (2017). The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms. Information, Communication & Society, 20(1), 30–44. 

This article reflects the kinds of situations and spaces where people and algorithms meet. In what situations do people become aware of algorithms? How do they experience and make sense of these algorithms, given their often hidden and invisible nature? To what extent does an awareness of algorithms affect people’s use of these platforms, if at all? To help answer these questions, this article examines people’s personal stories about the Facebook algorithm through tweets and interviews with 25 ordinary users. To understand the spaces where people and algorithms meet, this article develops the notion of the algorithmic imaginary. It is argued that the algorithmic imaginary – ways of thinking about what algorithms are, what they should be and how they function – is not just productive of different moods and sensations but plays a generative role in moulding the Facebook algorithm itself. Examining how algorithms make people feel, then, seems crucial if we want to understand their social power.

Eslami, M., Rickman, A., Vaccaro, K., Aleyasen, A., Vuong, A., Karahalios, K., … Sandvig, C. (2015). “I Always Assumed That I Wasn’T Really That Close to [Her]”: Reasoning About Invisible Algorithms in News Feeds. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 153–162). New York, NY, USA: ACM.

Our daily digital life is full of algorithmically selected content such as social media feeds, recommendations and personalized search results. These algorithms have great power to shape users’ experiences, yet users are often unaware of their presence. Whether it is useful to give users insight into these algorithms’ existence or functionality and how such insight might affect their experience are open questions. To address them, we conducted a user study with 40 Facebook users to examine their perceptions of the Facebook News Feed curation algorithm. Surprisingly, more than half of the participants (62.5%) were not aware of the News Feed curation algorithm’s existence at all. Initial reactions for these previously unaware participants were surprise and anger. We developed a system, FeedVis, to reveal the difference between the algorithmically curated and an unadulterated News Feed to users, and used it to study how users perceive this difference. Participants were most upset when close friends and family were not shown in their feeds. We also found participants often attributed missing stories to their friends’ decisions to exclude them rather than to Facebook News Feed algorithm. By the end of the study, however, participants were mostly satisfied with the content on their feeds. Following up with participants two to six months after the study, we found that for most, satisfaction levels remained similar before and after becoming aware of the algorithm’s presence, however, algorithmic awareness led to more active engagement with Facebook and bolstered overall feelings of control on the site.

Epstein, R., & Robertson, R. E. (2015). The search engine manipulation effect (SEME) and its possible impact on the outcomes of elections. Proceedings of the National Academy of Sciences, 112(33), E4512–E4521.

Internet search rankings have a significant impact on consumer choices, mainly because users trust and choose higher-ranked results more than lower-ranked results. Given the apparent power of search rankings, we asked whether they could be manipulated to alter the preferences of undecided voters in democratic elections. Here we report the results of five relevant double-blind, randomized controlled experiments, using a total of 4,556 undecided voters representing diverse demographic characteristics of the voting populations of the United States and India. The fifth experiment is especially notable in that it was conducted with eligible voters throughout India in the midst of India’s 2014 Lok Sabha elections just before the final votes were cast. The results of these experiments demonstrate that (i) biased search rankings can shift the voting preferences of undecided voters by 20% or more, (ii) the shift can be much higher in some demographic groups, and (iii) search ranking bias can be masked so that people show no awareness of the manipulation. We call this type of influence, which might be applicable to a variety of attitudes and beliefs, the search engine manipulation effect. Given that many elections are won by small margins, our results suggest that a search engine company has the power to influence the results of a substantial number of elections with impunity. The impact of such manipulations would be especially large in countries dominated by a single search engine company.

Gillespie, T. (2014). The Relevance of Algorithms. In: Media technologies essays on communication, materiality, and society. Cambridge, Mass.

Algorithms (particularly those embedded in search engines, social media platforms, recommendation systems, and information databases) play an increasingly important role in selecting what information is considered most relevant to us, a crucial feature of our participation in public life. As we have embraced computational tools as our primary media of expression, we are subjecting human discourse and knowledge to the procedural logics that undergird computation. What we need is an interrogation of algorithms as a key feature of our information ecosystem, and of the cultural forms emerging in their shadows, with a close attention to where and in what ways the introduction of algorithms into human knowledge practices may have political ramifications. This essay is a conceptual map to do just that. It proposes a sociological analysis that does not conceive of algorithms as abstract, technical achievements, but suggests how to unpack the warm human and institutional choices that lie behind them, to see how algorithms are called into being by, enlisted as part of, and negotiated around collective efforts to know and be known.

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2).

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms.

Pasquale, F. (2015). The Black Box Society: The Secret Algorithms That Control Money and Information. Cambridge, MA, USA: Harvard University Press.

Every day, corporations are connecting the dots about our personal behaviorsilently scrutinizing clues left behind by our work habits and Internet use. The data compiled and portraits created are incredibly detailed, to the point of being invasive. But who connects the dots about what firms are doing with this information? The Black Box Society argues that we all need to be able to do soand to set limits on how big data affects our lives. Hidden algorithms can make (or ruin) reputations, decide the destiny of entrepreneurs, or even devastate an entire economy. Shrouded in secrecy and complexity, decisions at major Silicon Valley and Wall Street firms were long assumed to be neutral and technical. But leaks, whistleblowers, and legal disputes have shed new light on automated judgment. Self-serving and reckless behavior is surprisingly common, and easy to hide in code protected by legal and real secrecy. Even after billions of dollars of fines have been levied, underfunded regulators may have only scratched the surface of this troubling behavior. Frank Pasquale exposes how powerful interests abuse secrecy for profit and explains ways to rein them in. Demanding transparency is only the first step. An intelligible society would assure that key decisions of its most important firms are fair, nondiscriminatory, and open to criticism. Silicon Valley and Wall Street need to accept as much accountability as they impose on others.

Proferes, N. (2017). Information Flow Solipsism in an Exploratory Study of Beliefs About Twitter. Social Media + Society, 3(1), 2056305117698493.

There is a dearth of research on the public’s beliefs about how social media technologies work. To help address this gap, this article presents the results of an exploratory survey that probes user and non-user beliefs about the techno-cultural and socioeconomic facets of Twitter. While many users are well-versed in producing and consuming information on Twitter, and understand Twitter makes money through advertising, the analysis reveals gaps in users’ understandings of the following: what other Twitter users can see or send, the kinds of user data Twitter collects through third parties, Twitter and Twitter partners’ commodification of user-generated content, and what happens to Tweets in the long term. This article suggests the concept of “information flow solipsism” as a way of describing the resulting subjective belief structure. The article discusses implications information flow solipsism has for users’ abilities to make purposeful and meaningful choices about the use and governance of social media spaces, to evaluate the information contained in these spaces, to understand how content users create is utilized by others in the short and long term, and to conceptualize what information other users experience.

Woolley, S. C., & Howard, P. N. (2016). Automation, Algorithms, and Politics| Political Communication, Computational Propaganda, and Autonomous Agents — Introduction. International Journal of Communication, 10(0), 9.

The Internet certainly disrupted our understanding of what communication can be, who does it, how, and to what effect. What constitutes the Internet has always been an evolving suite of technologies and a dynamic set of social norms, rules, and patterns of use. But the shape and character of digital communications are shifting again—the browser is no longer the primary means by which most people encounter information infrastructure. The bulk of digital communications are no longer between people but between devices, about people, over the Internet of things. Political actors make use of technological proxies in the form of proprietary algorithms and semiautomated social actors—political bots—in subtle attempts to manipulate public opinion. These tools are scaffolding for human control, but the way they work to afford such control over interaction and organization can be unpredictable, even to those who build them. So to understand contemporary political communication—and modern communication broadly—we must now investigate the politics of algorithms and automation.

 

How to measure media bias?

Introduction

Media bias is under heavy discussion at the moment, especially relating to the past presidential election in the US. However, the quality of discussion is not the way it should be; I mean, there should be objective analysis on the role of the media. Instead, most comments are politically motivated accusations or denials. This article aims to be objective, discussing the measurement of media bias; that is, how could we identify whether a particular media outlet is biased or not? The author feels there are not generally acknowledged measures for this, so it is easy to claim or deny bias without factual validation. Essentially, this erodes the quality of the discussion, leading only into a war of opinions. Second, without the existence of such measures, both the media and the general public are unable to monitor the fairness of coverage.

Why is media fairness important?

Fairness of the media is important for one main reason: the media have a strong influence on the public opinion. In other words, journalists have great power, and with great power comes great responsibility. The existence of bias leads to different standards of coverage depending on the topic being reported. In other words, the information is being used to portray a selective view of the world. This is analogous to confirmation bias; a person wants to prove a certain point, so he or she only acknowledges evidence supporting that point. Such behavior is very easy for human beings, for which reason journalists should be extra cautious in letting their own opinions influence the content of their reportage.

In addition to being a private problem, the media bias can also be understood as a systemic problem. This arises through 1) official guidelines and 2) informal group think. First, the official guidelines means that the opinions, beliefs or worldviews of the particular media outlet are diffused down the organization. Meaning that the editorial board communicates its official stance (”we, as a media outlet, support a political candidate X”) which is then taken by the individual reporters as their ethos. When the media outlet itself, or the surrounding ”media industry” as a whole, absorbs a view, there is a tendency to silence the dissidents. This, again, can be reduced to elementary human psychology, known as the conformity bias or group think. Because others in your reference group accept a certain viewpoint, you are more likely to accept it as well due to social pressure. The informal dynamics are even more dangerous to objective reporting than the official guidelines because they are subtle and implicit by nature. In other words, journalist may not be aware of bias and just consider their worldview ”normal” while arguments opposing it are classified as wrong and harmful.

Finally, media fairness is important due to its larger implications on information sources and the actions taken by citizens based on the information they are exposed to. It is in the society’s best interest that people resort to legitimate and trustworthy sources of information, as opposed to unofficial, rogue sources that can spread misinformation or disinformation. However, when the media becomes biased, it loses its legitimacy and becomes discredited; as a form of reactance to the biased stories, citizens turn to alternative sources of information. The problem is that these sources may not be trustworthy at all. Therefore, by waving their journalistic ethics, the mass media become at par with all other information sources; in a word, lose their credibility. The lack of credible sources of information leads into a myriad of problems for the society, such as distrust in the government, civil unrest or other forms of action people take based on the information they receive. Under such circumstances, the problem of ”echo chamber” is fortified — individuals feel free to select their sources according to their own beliefs instead of facts. After all, if all information is biased, what does it matter which one you choose to believe in?

How to measure media bias?

While it may not be difficult to define media bias at a general level, it may be difficult to observe an instance of bias in an unanimously acceptable way. That is where commonly accepted measures could be of some help. To come up with such measures, we can start by defining the information elements that can be retrieved for objectivity analysis. Then, we should consider how they can best be analyzed to determine whether a particular media outlet is biased.

In other words, what information do we have? Well, we can observe two sources: 1) the media itself, and 2) all other empirical observations (e.g., events taking place). Notice that observing the world only through media would be inaccurate testimony of human behavior; we draw a lot from our own experiences and from around us. By observing the stories created by the media we know what is being reported and what is not being reported. By observing things around us (apart from the media), we know what is happening and what is not happening. By combining these dimensions, we can derive

  1. what is being reported (and happens)
  2. what is being reported (but does not happen)
  3. what is not being reported (but happens), and
  4. what is not being reported (but does not happen).

Numbers 2 and 4 are not deemed relevant for this inquiry, but 1 and 3 are. Namely, the choice of information, i.e. what is being reported and what is being left out of reporting. Hence, this is the first dimension of our measurement framework.

1. Choice of information

  • topic inclusion — what topics are reported (themes –> identify, classify, count)
  • topic exclusion — what topics are not reported (reference –> define, classify, count)
  • story inclusion — what is included in the reportage (themes –> identify, classify, count)
  • story exclusion — what is left out of the reportage (reference –> define, classify, count)
  • story frequency — how many times a story is repeated (count)

This dimension measures what is being talked about in the media. It measures inclusion, exclusion and frequency to determine what information the media disseminates. The two levels are topics and stories — both have themes that can be identified, then material classified into them, and counted to get an understanding of the coverage. Measuring exclusion works in the same way, except the analyst needs to have a frame of reference he or she can compare the found themes with. For example, if the frame of reference contains ”Education” and the topics found from the material do not include education, then it can be concluded that the media at the period of sampling did not cover education. Besides themes, reference can include polarity, and thus one can examine if opposing views are given equal coverage. Finally, the frequency of stories measures media’s emphasis; reflecting the choice of information.

Because all information is selected from a close-to-infinite pool of potential stories, one could argue that all reportage is inherently biased. Indeed, there may not be universal criteria that would justify reporting Topic A over Topic B. However, measurement helps form a clearer picture of a) what the media as a whole is reporting, and b) what does each individual media outlet report in comparison to others. A member of the audience is then better informed on what themes the media has chosen to report. This type of helicopter view can enhance the ability to detect a biased information choice, either by a particular media outlet or the media as a whole.

The question of information choice is pertinent to media bias, especially relating to exclusion of information. A biased reporter can defend himself by arguing ”If I’m biased, show me where!”. But bias is not the same as inaccuracy. A biased story can still be accurate, for example, it may only leave some critical information out. The emphasis of a certain piece of information at the expense of other is a clear form of bias. Because not every piece of information can be included in a story, something is forcefully let out. Therefore, there is a temptation to favor a certain storyline. However, this concern can be neutralized by introducing balance; for a given topic, let there be an equal effort for exhibiting positive and negative evidence. And in terms of exclusion, discarding an equal amount of information from both extremes, if need be.

In addition to measuring what is being reported, we also need to consider how it is being reported. This is the second dimension of the measurement framework, dealing with the formulation of information.

2. Formulation of information

  • IN INTERVIEWS: question formulation — are the questions reporters are asking neutral or biased in terms of substance (identify, classify, count)
  • IN REPORTS: message formulation — are the paragraphs/sentences in reportage neutral or biased in terms of substance (classify, count)
  • IN INTERVIEWS: tone — is the tone reporters are asking the questions neutral or biased (classify count)
  • IN REPORTS: tone — are the paragraphs/sentences in reportage neutral or biased in terms of tone (classify, count)
  • loaded headlines (identify, count)
  • loaded vocabulary (identify, count)
  • general sentiment towards key objects (identify, classify: pos/neg/neutral)

This dimension measures how the media reports on the topics it has chosen. It is a form of content analysis, involving qualitative and quantitative features. Measures cover interview type of settings, as well as various reportages such as newspaper articles and television coverage. The content can be broken down into pieces (questions, paragraphs, sentences) and their objectivity evaluated based on both substance and tone. An example of bias in substance would be presenting an opinion as a fact, or taking a piece of information out of context. An example of biased tone would be using negative or positive adjectives in relation to select objects (e.g., presidential candidates).

Presenting loaded headlines and text as percentage of total observations gives an indication of how biased the content is. In addition, the analyst can evaluate the general sentiment the reportage portrays of key objects — this includes first identifying the key objects of the story, and then classifying their treatment on a three-fold scale (positive, negative, neutral).

I mentioned earlier that agreeing on the observation of bias is an issue. This is due to the interpretative nature of these measures; i.e., they involve a degree of subjectivity which is generally not considered as a good characteristic for a measure. Counting frequencies (e.g., how often a word was mentioned) is not susceptible to interpretation but judging the tone of the reporter is. Yet, those are the kind of cues that reveal a bias, so they should be incorporated in the measurement frameword. Perhaps we can draw an analogy to any form of research here; it is always up to the integrity of the analyst to draw conclusions.

Even studies that are said to include high reliability by design can be reported in a biased way, e.g. by reframing the original hypotheses. Ultimately, application of measurement in social sciences remains at the shoulder of the researcher. Any well-trained, committed researcher is more likely to follow the guideline of objectivity than not; but of course this cannot be guaranteed. The explication of method application should reveal to an outsider the degree of trustworthiness of the study, although the evaluation requires a degree of sophistication. Finally, using several analysts reduces an individual bias in interpreting content; inter-rater agreement can then be calculated with Cohen’s kappa or similar metrics.

After assessing the objectivity of the content, we turn to the source. Measurement of source credibility is important in both validating prior findings as well as understanding why the (potential) bias takes place.

3. Source credibility

  • individual political views (identify)
  • organizational political affiliation (identify)
  • reputation (sample)

This dimensions measures why the media outlet reports the way it does. If individual and organizational affiliations are not made clear in the reportage, the analyst needs to do work to discover them. In addition, the audience has shaped a perception of bias based on historical exposure to the media outlet — running a properly sampled survey can provide support information for conclusions of the objectivity study.

How to prevent media bias?

The work of journalists is sometimes compared to that of a scientist: in both professions, one needs curiousity, criticality, ability to observe, and objectivity. However, whereas scientists mostly report dull findings, reporters are much more pressured to write sexy, entertaining stories. This leads into the the problem of sense-making, i.e. reporters create a coherent story with a clear message, instead showing the messy reality. The sense-making bias in itself favors media bias, because creating a narrative forces one to be selective of what to include and what to exclude. As long as there is this desire for simple narratives, coverage of complex topics cannot be entirely objective. We may, however, mitigate this effect by upholding certain principles.

I suggest three principles for the media to uphold in their coverage of topics.

  • criticality
  • balance
  • objectivity
  • independence

First, the media should have a critical stance to its object of reportage. Instead of accepting the piece of information they receive as truth, they should push to ask hard questions. But that should be done in a balanced way – for example, in a presidential race, both candidates should get an equal amount of ”tough” questions. Furthermore, journalists should not absorb any ”truths”, beliefs or presumptions that affect in their treatment of a topic. Since every journalist is a human being, this requirement is quite an idealistic one; but the effect of personal preferences or those imposed by the social environment should in any case be mitigated. The goal of objectivity should be cherished, even if the outcome is in conflict with one’s personal beliefs. Finally, the media should be independent. Both in that it is not being dictated by any interest group, public or private, on what to report, but also in that it is not expressing or committing into a political affiliation. Much like church and state are kept separate according to Locke’s social contract as well as Jefferson’s constitutional ideas, the press and the state should be separated. This rule should apply to both publicly and privately funded media outlets.

Conclusion

The status of the media is precious. They have an enormous power over the opinions of the citizens. However, this is conditional power; should they lose objectivity, they’d also lose the influence, as people turn to alternative sources of information. I have presented that a major root cause of the problem is the media’s inability to detect its own bias. Through better detection and measurement of bias, corrective action can be taken. But since those corrective actions are conditioned to willingness to be objective, a willingness many media outlest are not signalling, the measurement in itself is not adequate in solving the larger problem. At a larger scale, I have proposed there be a separation of media and politics, which prevents by law any media outlet to take a political side. Such legislation is likely to increase objectivity and decrease the harmful polarization that the current partisan-based media environment constantly feeds into.

Overall, there should be some serious discusson on what the role of media in the society should be. In addition, attention to journalistic education and upholding of journalistic ethics should be paid. If the industry is not able to monitor itself, it is upon the society to introduce such regulation that the media will not abuse its power but remains objective. I have suggested the media and related stakeholders provide information on potential bias. I have also suggested new measures for bias that consider both the inclusion and exclusion of information. The measurement of inclusion can be done by analyzing news stories for common keywords and themes. If the analyst has an a prior framework of topics/themes/stories he or she considers as reference, it can be then concluded how well the media covers those themes by classifying the material accordingly. Such analysis would also reveal what is not being reported, an important distinction that is often not taken into account.

The Neutrality Dilemma in Machine Decision-Making

So, I read this article: Facebook is prioritizing my family and friends – but am I?

The point of the article — that you should focus on your friends & family in real life instead of Facebook — is poignant and topical. So much of our lives is spent on social media, without the ”social” part, and even when it is there, something is missing in comparison to physical presence (without smart phones!).

Anyway, this post is not about that. I got to think about the from the algorithm neutrality perspective. So what does that mean?

Algorithm neutrality takes place when social networks allow content spread freely based on its merits (e.g., CTR, engagement rate); so that the most popular content gets the most dissemination. In other words, the network imposes no media bias. Although the content spreading might have a media bias, the social network is objective and only accounting its quantifiable merits.

Why does this matter? Well, a neutral algorithm guarantees manipulation-free dissemination of information. As soon as human judgment intervenes, there is a bias. That bias may lead to censorship and favoring of certain political party, for example. The effect can be clearly seen in the so-called media bias. Anyone following either the political coverage of the US elections or the Brexit coverage has noticed the immense media bias which is omnipresent in even the esteemed publications, like the Economist and Washington Post. Indeed, they take a stance and report based on their stance, instead of covering objectively. A politically biased media like the one in the US is not much better than the politically biased media in Russia.

It is clear that free channels of expression enable the proliferation of alternative views, whereupon an individual is (theoretically) better off, since there are more data points to base his/her opinion on. Thus, social networks (again, theoretically) mitigate media bias.

There are many issues though. First is the one that I call neutrality dilemma.

The neutrality dilemma arises from what I already mentioned: the information bias can be embedded in the content people share. If the network restricts the information dissemination, it moves from neutrality to control. If it doesn’t restrict information dissemination, there is a risk of propagation of harmful misinformation, or propaganda. Therefore, in this continuum of control and freedom there is a trade-off that the social networks constantly need to address in their algorithms and community policies. For example, Facebook is banning some content, such as violent extremism. They are also collaborating with local governments which can ask for removal of certain content. This can be viewed in their transparency report.

The dilemma has multiple dimensions.

First of all, there are ethical issues. From the perspective of ”what is right”, shouldn’t the network prohibit diffusion of information when it is counter-factual? Otherwise, peopled can be mislead by false stories. But also, from perspective of what is right, shouldn’t there be free expression, even if a piece of information is not validated?

Second, there are some technical challenges:

A. How to identify ”truthfulness” of content? In many cases, it is seemingly impossible because the issues are complex and not factual to begin with. Consider e.g. the Brexit: it is not a fact that the leave vote would lead into a worse situation than the stay vote, and vice versa. In a similar vein, it is not a fact that the EU should be kept together. These are questions of assumptions which make them hard: people freely choose the assumptions they want to believe, but there can be no objective validation of this sort of complex social problem.

B. How to classify political/argumentative views and relate them to one another? There are different point of views, like ”pro-Brexit” and ”anti-Brexit”. The social network algorithm should detect based on an individual’s behavior their membership in a given group: the behavior consists of messages posted, content liked, shared and commented. It should be fairly easy to form a view of a person’s stance on a given topic with the help of these parameters. Then, it is crucial to map the stances in relation to one another, so that the extremes can be identified.

As it currently stands, one is being shown the content he/she prefers which confirms the already established opinion. This does not support learning or getting an objective view of the matter: instead, if reinforces a biased worldview and indeed exacerbates the problems. It is crucial to remember that opinions do not remain only opinions but reflect into behavior: what is socially established becomes physically established through people’s actions in the real world. Therefore, the power of social networks needs to be taken with precaution.

C. How to identify the quality of argumentation? Quality of argumentation is important if applying the rotation of alternative views intended to mitigate reinforcement of bias. This is because the counter-arguments need to be solid: in fact, when making a decision, the pro and contra-sides need both be well-argued for an objective decision to emerge. Machine learning could be the solution — assuming we have training data on the ”proper” structure of solid argumentation, we can compare this archetype to any kind of text material and assign it a score based on how good the argumentation is. Such a method does not consider the content of the argument, only its logical value. It would include a way to detect known argumentation errors based on syntax used. In fact, such a system is not unimaginably hard to achieve — common argumentation errors or logical fallacies are well documented.

Another form of detecting quality of argumentation is user-based reporting: individuals report the posts they don’t like, and these get discounted by the algorithm. However, Even when allowing users to report ”low-quality” content, there is a risk they report content they disagree with, not which is poorly argued. In reporting, there is relativism or subjectivism that cannot be avoided.

Perhaps the most problematic of all are the socio-psychological challenges associated with human nature. The neutral algorithm enforces group polarization by connecting people who agree on a topic. This is natural outcome of a neutral algorithm, since people by their behavior confirm their liking of a content they agree with. This leads to reinforcement whereupon they are shown more of that type of content. The social effect is known as group polarization – an individual’s original opinion is enforced through observing other individuals sharing that opinion. That is why so much discussion in social media is polarized: there is this well known tendency of human nature not to remain objective but to take a stance in one group against another.

How can we curb this effect? A couple of solutions readily come to mind.

1. Rotating opposing views. If in a neutral system you are shown 90% of content that confirms your beliefs, rotation should force you to see more than 10% percent of alternative (say, 25%). Technically, this would require that ”opinion archetypes” can be classified and contrasted to one another. Machine learning to the rescue?

The power of rotation comes from the idea it simulates social behavior: the more a person is exposed to subjects that initially seem strange and unlikeable (i.e., xenophobia), the more likely they are to be understood. A greater degree of awareness and understanding leads into higher acceptance of those things. In real world, people who frequently meet people from other cultures are more likely to accept other cultures in general.

Therefore, the same logic could by applied by Facebook in forcing us to see well-argumented counter-evidence to our beliefs. It is crucial that the counter-evidence is well-argued, or else there is a strong risk of reactance — people rejecting the opposing view even more. Unfortunately, this is a feature of the uneducated mind – not to be able to change one’s opinions but remain fixated on one’s beliefs. So the method is not full-proof, but it is better than what we now have.

2. Automatic fact-checking. Imagine a social network telling you ”This content might contain false information”. Caution signals may curb the willingness to accept any information. In fact, it may be more efficient to show misinformation tagged as unreliable rather than hide it — in the latter case, there is possibility for individuals to correct their false beliefs. Current approaches, however, rely on expert feedback which is fallible.

3. Research in sociology. I am not educated to know enough about the general solutions of group polarization, groupthink and other associated social problems. But I know sociologists have worked on them – this research should be put to use in collaboration with engineers who design the algorithms.

However, the root causes for dissemination of misinformation, either purposefully harmful or due to ignorance, lie not on technology. They are human-based problems and must have a human-based solution.

What are these root causes? Lack of education. Poor quality of educational system. Lack of willingness to study a topic before forming an opinion (i.e., lazy mind). Lack of source/media criticism. Confirmation bias. Groupthink. Group polarization.

Ultimately, these are the root causes of why some content that should not spread, spreads. They are social and psychological traits of human beings, which cannot be altered via algorithmic solutions. However, algorithms can direct behavior into more positive outcomes, or at least avoid the most harmful extremes – if the aforementioned classification problems can be solved.

The other part of the equation is education — kids need to be taught from early on about media and source criticism, logical argumentation, argumentation skills and respect to another party in a debate. Indeed, respect and sympathy go a long way — in the current atmosphere of online debating it seems like many have forgotten basic manners.

In the online environment, provocations are easy and escalate more easily than in face-to-face encounters. It is ”fun” to make fun of the ignorant people – a habit of the so-called intellectuals – nor it is correct to ignore science and facts – a habit of the so-called ignorants.

It is also unfortunate that many of the topics people debate on can be traced down to values and worldviews instead of more objective topics. When values and worldviews are fundamentally different among participants, it is truly hard to find a middle-way. It takes a lot of effort and character to be able to put yourself on the opposing party’s shoes, much more so than just point blank rejecting their view. It takes even more strength to change your opinion once you discover it was the wrong one.

Conclusion and discussion. Avoiding media bias is an essential advantage of social networks in information dissemination. I repeat: it’s a tremendous advantage. People are able to disseminate information and opinions without being controlled by mass-media outlets. At the same time, neutrality imposes new challenges. The most prominent question is to which extent should the network govern its content.

One one hand, user behavior is driving social networks like Facebook towards information sharing network – people are seemingly sharing more and more news content and less about their own lives – but Facebook wants to remain as social network, and therefore reduces neutrality in favor of personal content. What are the strategic implications? Will users be happier? Is it right to deviate from algorithm neutrality when you have dominant power over information flow?

Facebook is approaching a sort of an information monopoly when it comes to discovery (Google is the monopoly in information search), and I’d say it’s the most powerful global information dissemination medium today. That power comes with responsibility and ethical question, and hence the algorithm neutrality discussion. The strategic question for Facebook is that does it make sense for them to manipulate the natural information flow based on user behavior in a neutral system. The question for the society is should Facebook news feeds be regulated.

I am not advocating more regulation, since regulation is never a creative solution to any problem, nor does it tends to be informed by science. I advocate collaboration of sociologists and social networks in order to identify the best means to filter harmful misinformation and curb the generally known negative social tendencies that we humans possess. For sure, this can be done without endangering the free flow of information – the best part of social networks.