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 ideology of media. Measuring the political leaning of Spanish news media through Twitter users’ interactions

The news media have a strong influence on people’s perception of reality. But despite claims to objectivity, media organizations are, in general, politically biased (Patterson & Donsbach, 1996; Gaebler, 2017). The link between news media outlets and political organizations has been a critical question in political science and communication studies. To assess the closeness between the news media and particular political organizations, scholars have used different methods such as content analysis, undertaking surveys or adopting a political economy view. With the advent of social networks, new sources of data are now available to measure the relationship between media organizations and parties. Assuming that users coherently retweet political and news information (Wong, Tan, Sen & Chiang, 2016), and drawing on the retweet overlap network (RON) method (Guerrero-Solé, 2017), this research uses people’s perceived ideology of Spanish political parties (CIS, 2020) to propose a measure of the ideology of news media in Spain. Results show that scores align with the result of previous research on the ideology of the news media (Ceia, 2020). We also find that media outlets are, in general, politically polarized with two groups or clusters of news media being close to the left-wing parties UP and PSOE, and the other to the right-wing and far-right parties Cs, PP, and Vox. This research also underlines the media’s ideological stability over time.Los medios de comunicación tienen una fuerte influencia sobre la percepción de la realidad que tiene la gente. A pesar de su pretensión de objetividad, los medios tienen, en general, un sesgo político (Patterson & Donsbach, 1996; Gaebler, 2017). La relación entre los medios y las organizaciones políticas ha sido una cuestión crucial en los estudios de ciencias políticas y comunicación. Para evaluar la proximidad entre los medios de comunicación y organizaciones políticas concretas, los investigadores han empleado distintos métodos como el análisis de contenido, las encuestas o la adopción de una visión político-económica. Con la llegada de las redes sociales, aparecen nuevas fuentes de datos disponibles para medir la relación entre los medios de comunicación y los partidos políticos. Asumiendo que los usuarios retuitean coherentemente información política y mediática (Wong, Tan, Sen & Chiang, 2016), y haciendo uso del método RON (Retweet Overlap Network) (Guerrero-Solé, 2017), este estudio utiliza la ideología percibida por la población de los partidos políticos españoles (CIS, 2020) para proponer una medida de la ideología de los medios de comunicación en España. Los resultados muestran que las puntuaciones obtenidas siguen la línea de estudios realizados previamente sobre la ideología de los medios (Ceia, 2020). También se ha descubierto que los medios, en general, están polarizados políticamente, con dos grupos de medios más próximos a los partidos de izquierda UP y PSOE, y los otros a los partidos de derecha y ultraderecha Cs, PP y Vox. Esta investigación también remarca la estabilidad ideológica de los medios a lo largo del tiempo

Retweet-BERT: Political Leaning Detection Using Language Features and Information Diffusion on Social Networks

Estimating the political leanings of social media users is a challenging and ever more pressing problem given the increase in social media consumption. We introduce Retweet-BERT, a simple and scalable model to estimate the political leanings of Twitter users. Retweet-BERT leverages the retweet network structure and the language used in users' profile descriptions. Our assumptions stem from patterns of networks and linguistics homophily among people who share similar ideologies. Retweet-BERT demonstrates competitive performance against other state-of-the-art baselines, achieving 96%-97% macro-F1 on two recent Twitter datasets (a COVID-19 dataset and a 2020 United States presidential elections dataset). We also perform manual validation to validate the performance of Retweet-BERT on users not in the training data. Finally, in a case study of COVID-19, we illustrate the presence of political echo chambers on Twitter and show that it exists primarily among right-leaning users. Our code is open-sourced and our data is publicly available.Comment: 11 pages, 3 figures, 4 tables. arXiv admin note: text overlap with arXiv:2103.1097

Search Bias Quantification: Investigating Political Bias in Social Media and Web Search

Users frequently use search systems on the Web as well as online social media to learn about ongoing events and public opinion on personalities. Prior studies have shown that the top-ranked results returned by these search engines can shape user opinion about the topic (e.g., event or person) being searched. In case of polarizing topics like politics, where multiple competing perspectives exist, the political bias in the top search results can play a significant role in shaping public opinion towards (or away from) certain perspectives. Given the considerable impact that search bias can have on the user, we propose a generalizable search bias quantification framework that not only measures the political bias in ranked list output by the search system but also decouples the bias introduced by the different sources—input data and ranking system. We apply our framework to study the political bias in searches related to 2016 US Presidential primaries in Twitter social media search and find that both input data and ranking system matter in determining the final search output bias seen by the users. And finally, we use the framework to compare the relative bias for two popular search systems—Twitter social media search and Google web search—for queries related to politicians and political events. We end by discussing some potential solutions to signal the bias in the search results to make the users more aware of them.publishe

Sentiment Analysis and Political Party Classification in 2016 U.S. President Debates in Twitter

We introduce a framework of combining tweet sentiment analysis with available default user profiles to classify political party of users who posted tweets in 2016 U.S. president debates. The main works focus on extracting event-related information in short event period instead of collecting tweets in a long-time period as most previous works do. Our framework is not limited in debate event, it can be used by researchers to build rationale of other events study. In sentiment analysis, we show that all three Naïve Bayes classifiers with different distributions obtain accuracy above 75% and the results reveal positive tweets most likely follow Gaussian or Multinomial distributions while negative tweets most likely follow Bernoulli distribution in our training data. We also show that under unbalanced sparse term document setting, instead of using “Add-1” parameter, tuning Laplace smoothing parameter to adjust the weights of new terms in a tweet can help improve the classifier’s performance in targeted direction. Finally, we show sentiment might help classifying political part

Multi-Party Media Partisanship Attention Score. Estimating Partisan Attention of News Media Sources Using Twitter Data in the Lead-up to 2018 Italian Election

The ongoing radical transformations in communication ecosystems have brought up concerns about the risks of partisan selective exposure and ideological polarization. Tra- ditionally, partisan selective exposure is measured by cross-tabulating survey responses to questions on vote intentions and media consumption. This process is expensive, limits the number of news outlets taken into account and is prone to the typical biases of self-reported data. Building upon previous works and with a specific focus on the online media environment, we introduce a new method to measure partisan media attention in a multi-party political system using Twitter data from 2018 Italian general election. Our first research question addresses the effectiveness of this method by measuring the extent to which our estimates correlate with partisan newspaper consumption measured by the latest Italian National Election Studies (ITANES) survey. Once established the reliability of our method, we employ these scores and measures to analyze the Italian digital media ecosystem in the lead-up to March 2018 election. The traditionally high level of political parallelism that characterizes both the Italian press and TV sectors is only partially reflected in a digital media ecosystem where partisan news sources seem to coexist with cross-partisan outlets. Results also point out that certain online partisan communities tend to rely more on exclusive news media sources

Political Issue or Public Health: the Vaccination Debate on Twitter in Europe

At the beginning of the COVID-19 pandemic, fears grew that making vaccination a political (instead of public health) issue may impact the efficacy of this life-saving intervention, spurring the spread of vaccine-hesitant content. In this study, we examine whether there is a relationship between the political interest of social media users and their exposure to vaccine-hesitant content on Twitter. We focus on 17 European countries using a multilingual, longitudinal dataset of tweets spanning the period before COVID, up to the vaccine roll-out. We find that, in most countries, users' exposure to vaccine-hesitant content is the highest in the early months of the pandemic, around the time of greatest scientific uncertainty. Further, users who follow politicians from right-wing parties, and those associated with authoritarian or anti-EU stances are more likely to be exposed to vaccine-hesitant content, whereas those following left-wing politicians, more pro-EU or liberal parties, are less likely to encounter it. Somewhat surprisingly, politicians did not play an outsized role in the vaccine debates of their countries, receiving a similar number of retweets as other similarly popular users. This systematic, multi-country, longitudinal investigation of the connection of politics with vaccine hesitancy has important implications for public health policy and communication.Comment: 15 pages, 11 figure