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

Adversarial Graph Embeddings for Fair Influence Maximization over Social Networks

Influence maximization is a widely studied topic in network science, where the aim is to reach the maximum possible number of nodes, while only targeting a small initial set of individuals. It has critical applications in many fields, including viral marketing, information propagation, news dissemination, and vaccinations. However, the objective does not usually take into account whether the final set of influenced nodes is fair with respect to sensitive attributes, such as race or gender. Here we address fair influence maximization, aiming to reach minorities more equitably. We introduce Adversarial Graph Embeddings: we co-train an auto-encoder for graph embedding and a discriminator to discern sensitive attributes. This leads to embeddings which are similarly distributed across sensitive attributes. We then find a good initial set by clustering the embeddings. We believe we are the first to use embeddings for the task of fair influence maximization. While there are typically trade-offs between fairness and influence maximization objectives, our experiments on synthetic and real-world datasets show that our approach dramatically reduces disparity while remaining competitive with state-of-the-art influence maximization methods.Comment: In Proc. of the 29th International Joint Conference on Artificial Intelligence (IJCAI'20), 202

Facebook Ads Monitor: An Independent Auditing System for Political Ads on Facebook

The 2016 United States presidential election was marked by the abuse of targeted advertising on Facebook. Concerned with the risk of the same kind of abuse to happen in the 2018 Brazilian elections, we designed and deployed an independent auditing system to monitor political ads on Facebook in Brazil. To do that we first adapted a browser plugin to gather ads from the timeline of volunteers using Facebook. We managed to convince more than 2000 volunteers to help our project and install our tool. Then, we use a Convolution Neural Network (CNN) to detect political Facebook ads using word embeddings. To evaluate our approach, we manually label a data collection of 10k ads as political or non-political and then we provide an in-depth evaluation of proposed approach for identifying political ads by comparing it with classic supervised machine learning methods. Finally, we deployed a real system that shows the ads identified as related to politics. We noticed that not all political ads we detected were present in the Facebook Ad Library for political ads. Our results emphasize the importance of enforcement mechanisms for declaring political ads and the need for independent auditing platforms

The Road to Popularity: The Dilution of Growing Audience on Twitter

On social media platforms, like Twitter, users are often interested in gaining more influence and popularity by growing their set of followers, aka their audience. Several studies have described the properties of users on Twitter based on static snapshots of their follower network. Other studies have analyzed the general process of link formation. Here, rather than investigating the dynamics of this process itself, we study how the characteristics of the audience and follower links change as the audience of a user grows in size on the road to user's popularity. To begin with, we find that the early followers tend to be more elite users than the late followers, i.e., they are more likely to have verified and expert accounts. Moreover, the early followers are significantly more similar to the person that they follow than the late followers. Namely, they are more likely to share time zone, language, and topics of interests with the followed user. To some extent, these phenomena are related with the growth of Twitter itself, wherein the early followers tend to be the early adopters of Twitter, while the late followers are late adopters. We isolate, however, the effect of the growth of audiences consisting of followers from the growth of Twitter's user base itself. Finally, we measure the engagement of such audiences with the content of the followed user, by measuring the probability that an early or late follower becomes a retweeter

On the Users' Efficiency in the Twitter Information Network

Social media systems have increasingly become digi-tal information marketplaces, where users produce, con-sume and share information and ideas, often of public interest. In this context, social media users are their own curators of information – however, they can only select their information sources, who they follow, but cannot choose the information they are exposed to, which con-tent they receive. A natural question is thus to assess how efficient are users at selecting their information sources. In this work, we model social media users as information processing systems whose goal is acquiring a set of (unique) pieces of information. We then define a computational framework, based on minimal set co-vers, that allows us to evaluate every user’s performance as information curators within the system. Our frame-work is general and applicable to any social media sys-tem where every user follows others within the system to receive the information they produce. We leverage our framework to investigate the efficiency of Twitter users at acquiring information. We find that user’s efficiency typically decreases with respect to the number of people she follows. A more efficient user tends to be less overloaded and, as a consequence, any particular piece of information lives longer in the top of her timeline, thus facilitating her to actually read the information. Finally, while most unique information a user receives could have been acquired through a few users, less popular information requires following many different users

Analyzing Biases in Perception of Truth in News Stories and Their Implications for Fact Checking

Misinformation on social media has become a critical problem, particularly during a public health pandemic. Most social platforms today rely on users' voluntary reports to determine which news stories to fact-check first. Despite the importance, no prior work has explored the potential biases in such a reporting process. This work proposes a novel methodology to assess how users perceive truth or misinformation in online news stories. By conducting a large-scale survey (N = 15,000), we identify the possible biases in news perceptions and explore how partisan leanings influence the news selection algorithm for fact checking. Our survey reveals several perception biases or inaccuracies in estimating the truth level of stories. The first kind, called the total perception bias (TPB), is the aggregate difference in the ground truth and perceived truth level. The next two are the false-positive bias (FPB) and false-negative bias (FNB), which measures users' gullibility and cynicality of a given claim. We also propose ideological mean perception bias (IMPB), which quantifies a news story's ideological disputability. Collectively, these biases indicate that user perceptions are not correlated with the ground truth of new stories; users believe some stories to be more false and vice versa. This calls for the need to fact-check news stories that exhibit the most considerable perception biases first, which the current voluntary reporting does not offer. Based on these observations, we propose a new framework that can best leverage users' truth perceptions to remove false stories, correct misperceptions of users, or decrease ideological disagreements. We discuss how this new prioritizing scheme can aid platforms to significantly reduce the impact of fake news on user beliefs.11Nsciescopu

Facebook Ads Monitor: An Independent Auditing System for Political Ads on Facebook

International audienc