The Problem of Fake News

Looking at the recent spate of claims about “fake news” which appear to be a new feature of political discourse, I argue that fake news presents an interesting problem in epistemology. Te phenomena of fake news trades upon tolerating a certain indiference towards truth, which is sometimes expressed insincerely by political actors. Tis indiference and insincerity, I argue, has been allowed to fourish due to the way in which we have set the terms of the “public” epistemology that maintains what is considered “rational” public discourse. I argue one potential salve to the problem of fake news is to challenge this public epistemology by injecting a certain ethical consideration back into the discourse

Computational Controversy

Climate change, vaccination, abortion, Trump: Many topics are surrounded by fierce controversies. The nature of such heated debates and their elements have been studied extensively in the social science literature. More recently, various computational approaches to controversy analysis have appeared, using new data sources such as Wikipedia, which help us now better understand these phenomena. However, compared to what social sciences have discovered about such debates, the existing computational approaches mostly focus on just a few of the many important aspects around the concept of controversies. In order to link the two strands, we provide and evaluate here a controversy model that is both, rooted in the findings of the social science literature and at the same time strongly linked to computational methods. We show how this model can lead to computational controversy analytics that have full coverage over all the crucial aspects that make up a controversy.Comment: In Proceedings of the 9th International Conference on Social Informatics (SocInfo) 201

The Effect of Collective Attention on Controversial Debates on Social Media

We study the evolution of long-lived controversial debates as manifested on Twitter from 2011 to 2016. Specifically, we explore how the structure of interactions and content of discussion varies with the level of collective attention, as evidenced by the number of users discussing a topic. Spikes in the volume of users typically correspond to external events that increase the public attention on the topic -- as, for instance, discussions about `gun control' often erupt after a mass shooting. This work is the first to study the dynamic evolution of polarized online debates at such scale. By employing a wide array of network and content analysis measures, we find consistent evidence that increased collective attention is associated with increased network polarization and network concentration within each side of the debate; and overall more uniform lexicon usage across all users.Comment: accepted at ACM WebScience 201

The Ebb and Flow of Controversial Debates on Social Media

We explore how the polarization around controversial topics evolves on Twitter - over a long period of time (2011 to 2016), and also as a response to major external events that lead to increased related activity. We find that increased activity is typically associated with increased polarization; however, we find no consistent long-term trend in polarization over time among the topics we study.Comment: Accepted as a short paper at ICWSM 2017. Please cite the ICWSM version and not the ArXiv versio

All Together Now: Collaboration and Innovation for Youth Engagement

Each new generation must become active, informed, responsible, and effective citizens. As a teacher we surveyed for this report said, civic education "is essential if we are to continue as a free democratic society. Not to educate the next generation will ensure the destruction of our American way of life as we know it."Data show that many young Americans are reasonably well informed and active. For instance, 45% of citizens between the ages of 18 and 29 voted in the 2012 election. In a national survey conducted for this Commission, 76% of people under the age of 25 who voted could correctly answer at least one (out of two) factual questions about where the presidential candidates stood on a campaign issue and state their own opinion on that issue.On the other hand, more than half of young people did not vote. And on some topics, most young people were misinformed. A majority (51.2%) of under 25-year olds believed that the federal government spends more on foreign aid than on Social Security, when in fact Social Security costs about 20 times more. (Older adults have also been found to be misinformed on similar topics.) Our research, like many other studies, finds that young people from disadvantaged backgrounds are far less likely to be informed and to vote.These shortcomings cannot be attributed to the schools alone, since families, friends, political campaigns, election officials, the mass media, social media, and community-based organizations are among the other important influences on young people. In fact, our research shows that while schools matter, civic education must be a shared responsibility.The outcomes are acceptable only when all the relevant institutions invite, support, and educate young people to engage in politics and civic life. Improving the quality and quantity of youth participation will require new collaborations; for example, state election officials and schools should work together to make voting procedures understandable and to educate students about voting rules

Factors in Recommending Contrarian Content on Social Media

Polarization is a troubling phenomenon that can lead to societal divisions and hurt the democratic process. It is therefore important to develop methods to reduce it. We propose an algorithmic solution to the problem of reducing polarization. The core idea is to expose users to content that challenges their point of view, with the hope broadening their perspective, and thus reduce their polarity. Our method takes into account several aspects of the problem, such as the estimated polarity of the user, the probability of accepting the recommendation, the polarity of the content, and popularity of the content being recommended. We evaluate our recommendations via a large-scale user study on Twitter users that were actively involved in the discussion of the US elections results. Results shows that, in most cases, the factors taken into account in the recommendation affect the users as expected, and thus capture the essential features of the problem.Comment: accepted as a short paper at ACM WebScience 2017. arXiv admin note: substantial text overlap with arXiv:1703.1093

Analysing Controversy on Twitter via Graph Embeddings

Social networks represent a public forum of discussion for various topics, some of them controversial. Twitter is such a social network; it acts as a public space where discourse occurs. In recent years the role of social networks in information spreading has increased. As have the fears regarding the increasingly polarised discourse on social networks, caused by the tendency of users to avoid exposure to opposing opinions, while increasingly interacting with only like-minded individuals. This work looks at controversial topics on Twitter, over a long period of time, through the prism of political polarisation. We use the daily interactions, and the underlying structure of the whole conversation, to create daily graphs that are then used to obtain daily graph embeddings. We estimate the political ideologies of the users that are represented in the graph embeddings. By using the political ideologies of users and the daily graph embeddings, we offer a series of methods that allow us to detect and analyse changes in the political polarisation of the conversation. This enables us to conclude that, during our analysed time period, the overall polarisation levels for our examined controversial topics have stagnated. We also explore the effects of topic-related controversial events on the conversation, thus revealing their short-term effect on the conversation as a whole. Additionally, the linkage between increased interest in a topic and the increase of political polarisation is explored. Our findings reveal that as the interest in the controversial topic increases, so does the political polarisation

Russian Twitter disinformation campaigns reach across the American political spectrum

Evidence from an analysis of Twitter data reveals that Russian social media trolls exploited racial and political identities to infiltrate distinct groups of authentic users, playing on their group identities. The groups affected spanned the ideological spectrum, suggesting the importance of coordinated counter-responses from diverse coalitions of users

Quantifying and minimizing risk of conflict in social networks

Controversy, disagreement, conflict, polarization and opinion divergence in social networks have been the subject of much recent research. In particular, researchers have addressed the question of how such concepts can be quantified given people’s prior opinions, and how they can be optimized by influencing the opinion of a small number of people or by editing the network’s connectivity. Here, rather than optimizing such concepts given a specific set of prior opinions, we study whether they can be optimized in the average case and in the worst case over all sets of prior opinions. In particular, we derive the worst-case and average-case conflict risk of networks, and we propose algorithms for optimizing these. For some measures of conflict, these are non-convex optimization problems with many local minima. We provide a theoretical and empirical analysis of the nature of some of these local minima, and show how they are related to existing organizational structures. Empirical results show how a small number of edits quickly decreases its conflict risk, both average-case and worst-case. Furthermore, it shows that minimizing average-case conflict risk often does not reduce worst-case conflict risk. Minimizing worst-case conflict risk on the other hand, while computationally more challenging, is generally effective at minimizing both worst-case as well as average-case conflict risk