Information consumption on social media : efficiency, divisiveness, and trust

Over the last decade, the advent of social media has profoundly changed the way people produce and consume information online. On these platforms, users themselves play a role in selecting the sources from which they consume information, overthrowing traditional journalistic gatekeeping. Moreover, advertisers can target users with news stories using users’ personal data. This new model has many advantages: the propagation of news is faster, the number of news sources is large, and the topics covered are diverse. However, in this new model, users are often overloaded with redundant information, and they can get trapped in filter bubbles by consuming divisive and potentially false information. To tackle these concerns, in my thesis, I address the following important questions: (i) How efficient are users at selecting their information sources? We have defined three intuitive notions of users’ efficiency in social media: link, in-flow, and delay efficiency. We use these three measures to assess how good users are at selecting who to follow within the social media system in order to most efficiently acquire information. (ii) How can we break the filter bubbles that users get trapped in? Users on social media sites such as Twitter often get trapped in filter bubbles by being exposed to radical, highly partisan, or divisive information. To prevent users from getting trapped in filter bubbles, we propose an approach to inject diversity in users’ information consumption by identifying non-divisive, yet informative information. (iii) How can we design an efficient framework for fact-checking? Proliferation of false information is a major problem in social media. To counter it, social media platforms typically rely on expert fact-checkers to detect false news. However, human fact-checkers can realistically only cover a tiny fraction of all stories. So, it is important to automatically prioritizing and selecting a small number of stories for human to fact check. However, the goals for prioritizing stories for fact-checking are unclear. We identify three desired objectives to prioritize news for fact-checking. These objectives are based on the users’ perception of truthfulness of stories. Our key finding is that these three objectives are incompatible in practice.In den letzten zehn Jahren haben soziale Medien die Art und Weise, wie Menschen online Informationen generieren und konsumieren, grundlegend verändert. Auf Social Media Plattformen wählen Nutzer selbst aus, von welchen Quellen sie Informationen beziehen hebeln damit das traditionelle Modell journalistischen Gatekeepings aus. Zusätzlich können Werbetreibende Nutzerdaten dazu verwenden, um Nachrichtenartikel gezielt an Nutzer zu verbreiten. Dieses neue Modell bietet einige Vorteile: Nachrichten verbreiten sich schneller, die Zahl der Nachrichtenquellen ist größer, und es steht ein breites Spektrum an Themen zur Verfügung. Das hat allerdings zur Folge, dass Benutzer häufig mit überflüssigen Informationen überladen werden und in Filterblasen geraten können, wenn sie zu einseitige oder falsche Informationen konsumieren. Um diesen Problemen Rechnung zu tragen, gehe ich in meiner Dissertation auf die drei folgenden wichtigen Fragestellungen ein: • (i) Wie effizient sind Nutzer bei der Auswahl ihrer Informationsquellen? Dazu definieren wir drei verschiedene, intuitive Arten von Nutzereffizienz in sozialen Medien: Link-, In-Flowund Delay-Effizienz. Mithilfe dieser drei Metriken untersuchen wir, wie gut Nutzer darin sind auszuwählen, wem sie auf Social Media Plattformen folgen sollen um effizient an Informationen zu gelangen. • (ii) Wie können wir verhindern, dass Benutzer in Filterblasen geraten? Nutzer von Social Media Webseiten werden häufig Teil von Filterblasen, wenn sie radikalen, stark parteiischen oder spalterischen Informationen ausgesetzt sind. Um das zu verhindern, entwerfen wir einen Ansatz mit dem Ziel, den Informationskonsum von Nutzern zu diversifizieren, indem wir Informationen identifizieren, die nicht polarisierend und gleichzeitig informativ sind. • (iii) Wie können wir Nachrichten effizient auf faktische Korrektheit hin überprüfen? Die Verbreitung von Falschinformationen ist eines der großen Probleme sozialer Medien. Um dem entgegenzuwirken, sind Social Media Plattformen in der Regel auf fachkundige Faktenprüfer zur Identifizierung falscher Nachrichten angewiesen. Die manuelle Überprüfung von Fakten kann jedoch realistischerweise nur einen sehr kleinen Teil aller Artikel und Posts abdecken. Daher ist es wichtig, automatisch eine überschaubare Zahl von Artikeln für die manuellen Faktenkontrolle zu priorisieren. Nach welchen Zielen eine solche Priorisierung erfolgen soll, ist jedoch unklar. Aus diesem Grund identifizieren wir drei wünschenswerte Priorisierungskriterien für die Faktenkontrolle. Diese Kriterien beruhen auf der Wahrnehmung des Wahrheitsgehalts von Artikeln durch Nutzer. Unsere Schlüsselbeobachtung ist, dass diese drei Kriterien in der Praxis nicht miteinander vereinbar sind

The Power of Related Articles – Improving Fake News Detection on Social Media Platforms

Social media is increasingly used as a platform for news consumption, but it has also become a breeding ground for fake news. This serious threat poses significant challenges to social media providers, society, and science. Several studies have investigated automated approaches to fighting fake news, but little has been done to improve fake news detection on the users’ side. A simple but promising approach could be to broaden users\u27 knowledge to improve the perceptual process, which will improve detection behavior. This study evaluates the impact of a digital nudging approach which aims to fight fake news with the help of related articles. 322 participants took part in an online experiment simulating the Facebook Newsfeed. In addition to a control group, three treatment groups were exposed to different combinations of related articles. Results indicate that the presence of controversial related articles has a positive influence on the detection of fake news

Factual or Believable? Negotiating the Boundaries of Confirmation Bias in Online News Stories

We examine the fake news phenomenon from a fresh perspective. Instead of assessing the factuality of news claims, our work explores the impact of these claims on reader beliefs. With the 2017 Alabama senate race as the empirical context, we examine how readers on both sides of the political spectrum evaluate online news stories considering their preconceived beliefs and values. Our analysis builds on concepts from argument and social representations theories to explore the role of argumentation in this process. We focus on detecting arguments in reader comments to depict challenges involved in reader consideration of newsworthy events and news stories. A key finding of the paper is that readers from both sides of the political spectrum appear to engage in similar strategies to confirm or negotiate acceptance or rejection of claims. The paper contributes to theory by depicting social representation as a process that mediates conflict in belief structures. We conclude by speculating about possibilities for future work, such as designing behavioral and technological interventions that can supplement fact-checking. An important goal here is to improve how we, in the presence of our biases, collectively consume online news stories and engage in the discourse that surrounds them

Gender biases in fake news : how is gender employed in fake news against female candidates?

The objective of this study is to analyze how gender is employed in fake news against female candidates. Fake news is not an entirely new problem, however the internet has allowed for its extensive and quick diffusion, which presents new challenges. According to Shao et al. (2017) the widespread reach of fake news is a major global risk; in that it may affect election outcomes and threaten democracies. One of the ways an election result may be influenced, is if fake news containing gendered attacks against female candidates is widespread. Women pursuing high-level positions of power normally associated with men have often been the target of prejudice, because their candidacy goes against the gendered social norms still existent in our society (Manne, 2018). This leaves women in politics at a disadvantage from the beginning, given that men and women possessing the same attributes or carrying out the same kinds of actions may be perceived differently by voters or get a different reaction from the public (Manne, 2018). The 2016 U.S. presidential election, which was a turning point in terms of widespread concern over the impact of fake news in modern democracies, is used as a case study to investigate questions of gender biases in politics, and the portrayals of female candidates in fake news. This study uses a qualitative content analysis of over 100 fake news stories, independently verified as “false” by a fact-checking organization, that mention Hillary Clinton and/or Donald Trump, and that were spread in 2016, in order to identify potential gender-related patterns in the quantity, type or topic of the most shared fake news stories. The results of this study show that fake news content, disseminated during this time period, often played on gender biases already engrained in society to benefit or disparage different candidates. This study contributes to the fight against fake news, by helping show how gender is also being used in the fabrication of fake news content, to manipulate and influence social media users, and potentially impact election results. Existing gender stereotypes regarding political candidates seem to be used in fake news to hinder female candidates

Digital Media Literacy In the Age of Mis/Disinformation: The Case of Moroccan University Students

This paper set out to explore online users' perceptions, attitudes, and practices towards mis/disinformation on social networking sites and investigate how they engage with, identify, and evaluate information disorder on social networking sites. The correlation study provides empirical insights into the complex relationship between digital media literacy and online information processing. To this end, a web-based survey was administered to gauge Moroccan undergraduate students' digital media literacy skills, particularly in what regards their ability to identify and evaluate the credibility of information online. The data obtained are consistent with the hypothesis guiding this research that there is a significant relationship between digital media literacy skills (DMLS) and students' ability to identify information disorder online (IDO). Based on the empirical findings, important implications and strategies for higher education institutions are addressed to help students become more digitally media literate consumers of information.

Fake News and the Tax Law

The public misunderstands many aspects of the tax system. For example, people frequently misunderstand how marginal tax rates work, misperceive their own average tax rates, and believe they benefit from tax deductions for which they are ineligible. Such confusion is understandable given the complexity of our tax laws. Unfortunately, research suggests these misconceptions shape voter preferences about tax policy which, in turn, impact the policies themselves. That people are easily confused by taxes is nothing new. With the rise of social media platforms, however, the speed at which misinformation campaigns can now move to shape public opinion is far faster. The past five years have seen a dramatic shift in the landscape of false information and scholars in a variety of disciplines, from law to psychology to journalism, have explored the increasing influence of fake news. Building on this burgeoning literature, this Article is the first to examine the incidence and impact of fake news on tax law. We analyze a unique dataset of tax stories flagged as “false” or “untrue” by reputable, third-party news sources. We use this dataset to explore common themes in fake tax news, as well as the ways tax laws’ complexity contributes to spreading false information. We then offer recommendations for how tax administrators and policymakers can combat these misinformation efforts. Specifically, we argue that insights from the literature on fake news can and should inform how administrators disseminate true tax information to the public. Further, understanding what types of tax laws are easily misunderstood or subject to manipulation should inform substantive tax policy design

Fake News and the Tax Law

The public misunderstands many aspects of the tax system. For example, people frequently misunderstand how marginal tax rates work, misperceive their own average tax rates, and believe they benefit from tax deductions for which they are ineligible. Such confusion is understandable given the complexity of our tax laws. Unfortunately, research suggests these misconceptions shape voter preferences about tax policy which, in turn, impact the policies themselves. That people are easily confused by taxes is nothing new. With the rise of social media platforms, however, the speed at which misinformation campaigns can now move to shape public opinion is far faster. The past five years have seen a dramatic shift in the landscape of false information and scholars in a variety of disciplines, from law to psychology to journalism, have explored the increasing influence of fake news. Building on this burgeoning literature, this Article is the first to examine the incidence and impact of fake news on tax law. We analyze a unique dataset of tax stories flagged as “false” or “untrue” by reputable, third-party news sources. We use this dataset to explore common themes in fake tax news, as well as the ways tax laws’ complexity contributes to spreading false information. We then offer recommendations for how tax administrators and policymakers can combat these misinformation efforts. Specifically, we argue that insights from the literature on fake news can and should inform how administrators disseminate true tax information to the public. Further, understanding what types of tax laws are easily misunderstood or subject to manipulation should inform substantive tax policy design