Towards Measuring Adversarial Twitter Interactions against Candidates in the US Midterm Elections

Adversarial interactions against politicians on social media such as Twitter have significant impact on society. In particular they disrupt substantive political discussions online, and may discourage people from seeking public office. In this study, we measure the adversarial interactions against candidates for the US House of Representatives during the run-up to the 2018 US general election. We gather a new dataset consisting of 1.7 million tweets involving candidates, one of the largest corpora focusing on political discourse. We then develop a new technique for detecting tweets with toxic content that are directed at any specific candidate.Such technique allows us to more accurately quantify adversarial interactions towards political candidates. Further, we introduce an algorithm to induce candidate-specific adversarial terms to capture more nuanced adversarial interactions that previous techniques may not consider toxic. Finally, we use these techniques to outline the breadth of adversarial interactions seen in the election, including offensive name-calling, threats of violence, posting discrediting information, attacks on identity, and adversarial message repetition

Twitter and social bots : an analysis of the 2021 Canadian election

Les médias sociaux sont désormais des outils de communication incontournables, notamment lors de campagnes électorales. La prévalence de l’utilisation de plateformes de communication en ligne suscite néanmoins des inquiétudes au sein des démocraties occidentales quant aux risques de manipulation des électeurs, notamment par le biais de robots sociaux. Les robots sociaux sont des comptes automatisés qui peuvent être utilisés pour produire ou amplifier le contenu en ligne tout en se faisant passer pour de réels utilisateurs. Certaines études, principalement axées sur le cas des États-Unis, ont analysé la propagation de contenus de désinformation par les robots sociaux en période électorale, alors que d’autres ont également examiné le rôle de l’affiliation partisane sur les comportements et les tactiques favorisées par les robots sociaux. Toutefois, la question à savoir si l'orientation partisane des robots sociaux a un impact sur la quantité de désinformation politique qu’ils propagent demeure sans réponse. Par conséquent, l’objectif principal de ce travail de recherche est de déterminer si des différences partisanes peuvent être observées dans (i) le nombre de robots sociaux actifs pendant la campagne électorale canadienne de 2021, (ii) leurs interactions avec les comptes réels, et (iii) la quantité de contenu de désinformation qu’ils ont propagé. Afin d’atteindre cet objectif de recherche, ce mémoire de maîtrise s’appuie sur un ensemble de données Twitter de plus de 11,3 millions de tweets en anglais provenant d’environ 1,1 million d'utilisateurs distincts, ainsi que sur divers modèles pour distinguer les comptes de robots sociaux des comptes humains, déterminer l’orientation partisane des utilisateurs et détecter le contenu de désinformation politique véhiculé. Les résultats de ces méthodes distinctes indiquent des différences limitées dans le comportement des robots sociaux lors des dernières élections fédérales. Il a tout de même été possible d'observer que les robots sociaux de tendance conservatrice étaient plus nombreux que leurs homologues de tendance libérale, mais que les robots sociaux d’orientation libérale étaient ceux qui ont interagi le plus avec les comptes authentiques par le biais de retweets et de réponses directes, et qui ont propagé le plus de contenu de désinformation.Social media have now become essential communication tools, including within the context of electoral campaigns. However, the prevalence of online communication platforms has raised concerns in Western democracies about the risks of voter manipulation, particularly through social bot accounts. Social bots are automated computer algorithms which can be used to produce or amplify online content while posing as authentic users. Some studies, mostly focused on the case of the United States, analyzed the propagation of disinformation content by social bots during electoral periods, while others have also examined the role of partisanship on social bots’ behaviors and activities. However, the question of whether social bots’ partisan-leaning impacts the amount of political disinformation content they generate online remains unanswered. Therefore, the main goal of this study is to determine whether partisan differences could be observed in (i) the number of active social bots during the 2021 Canadian election campaign, (ii) their interactions with humans, and (iii) the amount of disinformation content they propagated. In order to reach this research objective, this master’s thesis relies on an original Twitter dataset of more than 11.3 million English tweets from roughly 1.1 million distinct users, as well as diverse models to distinguish between social bot and human accounts, determine the partisan-leaning of users, and detect political disinformation content. Based on these distinct methods, the results indicate limited differences in the behavior of social bots in the 2021 federal election. It was however possible to observe that conservative-leaning social bots were more numerous than their liberal-leaning counterparts, but liberal-leaning accounts were those who interacted more with authentic accounts through retweets and replies and shared the most disinformation content

A Look Through a Broken Window: The Relationship Between Disorder and Toxicity on Social Networking Sites

Toxicity has increased on social networking sites (SNSs), sparking a debate on its underlying causes. While research readily explored eligible social factors, disorder induced by the very nature of SNSs has been neglected so far. The relationship between disorder and deviant behaviors could be revealed within the offline sphere. Incorporating the theoretical lens of the Broken Windows Theory, we propose that a similar mechanism is prevalent in the online context. To test the hypothesis that perceived disorder increases toxicity on SNSs, the study compares two subcommunities on Reddit dedicated to the same topic that differ in their perceived disorder. Sampling the toxicity scores via data collection and natural language processing yields the first evidence for our hypothesis. We further outline subsequent studies that aim to investigate further the phenomenon of how disorder-related factors contribute to toxic online environments

The United States’ and United Kingdom’s Responses to 2016 Russian Election Interference: Through the Lens of Bureaucratic Politics

Russia’s 2016 disinformation campaign during the U.S. elections represented the first large-scale campaign against the United States and was intended to cause American citizens to question the fundamental security and resilience of U.S. democracy. A similar campaign during the 2016 U.K. Brexit referendum supported the campaign to leave the European Union. This paper assesses the policy formation process in the United States and United Kingdom in response to 2016 Russian disinformation using a bureaucratic politics framework. Focusing on the role of sub-state organizations in policy formation, the paper identifies challenges to establishing an effective policy response to foreign disinformation, particularly in the emergence of leadership and bargaining, and the impact of centralization of power in the U.K. Discussion of the shift in foreign policy context since the end of the Cold War, which provided a greater level of foreign policy consensus, as well as specific challenges presented by the cyber deterrence context, supplements insights from bureaucratic politics. Despite different governmental structures, both countries struggled to achieve collaborative and systematic policy processes; analysis reveals the lack of leadership and coordination in the United States and both the lack of compromise and effective fulfillment of responsibilities in the United Kingdom. Particular challenges of democracies responding to exercises of sharp power by authoritarian governments point to the need for a wholistic response from public and private entities and better definition of intelligence agencies’ responsibility to election security in the U.K

Computational Methods in the Study of Political Behavior

In this thesis, I explore how individual-level actions contribute to aggregate political outcomes. In each chapter, I aim to understand an observed political behavior using data or methodologies previously unused in their contexts. The subject matter ranges from protest activity and vote choice to theoretical opinion models and re-examining how socioeconomic class is understood in quantitative work. In the first two chapters I employ novel datasets to understand phenomena where popular theories differ from empirical observations. In Chapter 1 I examine protest behavior, which is not the equilibrium prediction of models of collective action. I investigate what aspects of published language can predict protest participation and how these change leading up to and following protests. Specifically, I collect and, using natural language processing methods, analyze 4 million tweets of individuals who participated in the Black Lives Matter protests during the summer of 2020. Using geographical and temporal variation to isolate results, I find evidence that interest in the subject, measured as percentage of online time discussing the matter, is correlated with protest behavior. However, I also find that collective identity, measured through pronoun use, does not have a strong relationship with protest behavior. Next, in Chapter 2, I use a survey---which I helped to develop and field---to understand the 2020 midterm elections' surprising results. While most accepted models of midterm elections predicted massive Democratic losses (averaging around 40 seats in the House), these predictions were not met. In fact, the Democratic party did well---they did not lose a single state legislature, expanded some majorities, and lost only 9 seats in the House of Representatives. Testing various models of midterm elections, I show that the 2020 midterms were issue-based elections, where views on abortion had a large impact on vote choice. In the second half of the thesis I focus on methodologies. Specifically, in Chapter 3, I expanded on mathematical models of consensus building to better mimic reality. Bounded confidence models have historically been used to explain convergence of opinions. In this chapter I add a repulsive element, modeling the inclination to differentiate oneself from someone who otherwise has similar beliefs. With this added component, convergence is no longer assumed. I explore both analytical and simulated numerical results to understand the dynamics of opinions in this new context. Finally, in Chapter 4, I introduce a method for operationalizing socioeconomic class as a latent variable in regression models. While there has been a plethora of research which shows that class affects opinions, views, and actions, the definition of class is nebulous. I argue that this is a result of the nature of class, which is context dependent. Therefore, rather than explicitly determining class, I present using class within a mixture model framework. This allows for the exact definition of class to change within the context being analyzed and enables researchers to use class within their work. Following the theoretical arguments, I present the efficacy of the approach using the American National Election Studies survey from 2020 to show how class differs when related to views of the U.S. Immigration and Customs Enforcement agency and the Black Lives Matter movement.</p

Data science methods for the analysis of controversial social dedia discussions

Social media communities like Reddit and Twitter allow users to express their views on topics of their interest, and to engage with other users who may share or oppose these views. This can lead to productive discussions towards a consensus, or to contended debates, where disagreements frequently arise. Prior work on such settings has primarily focused on identifying notable instances of antisocial behavior such as hate-speech and “trolling”, which represent possible threats to the health of a community. These, however, are exceptionally severe phenomena, and do not encompass controversies stemming from user debates, differences of opinions, and off-topic content, all of which can naturally come up in a discussion without going so far as to compromise its development. This dissertation proposes a framework for the systematic analysis of social media discussions that take place in the presence of controversial themes, disagreements, and mixed opinions from participating users. For this, we develop a feature-based model to describe key elements of a discussion, such as its salient topics, the level of activity from users, the sentiments it expresses, and the user feedback it receives. Initially, we build our feature model to characterize adversarial discussions surrounding political campaigns on Twitter, with a focus on the factual and sentimental nature of their topics and the role played by different users involved. We then extend our approach to Reddit discussions, leveraging community feedback signals to define a new notion of controversy and to highlight conversational archetypes that arise from frequent and interesting interaction patterns. We use our feature model to build logistic regression classifiers that can predict future instances of controversy in Reddit communities centered on politics, world news, sports, and personal relationships. Finally, our model also provides the basis for a comparison of different communities in the health domain, where topics and activity vary considerably despite their shared overall focus. In each of these cases, our framework provides insight into how user behavior can shape a community’s individual definition of controversy and its overall identity.Social-Media Communities wie Reddit und Twitter ermöglichen es Nutzern, ihre Ansichten zu eigenen Themen zu äußern und mit anderen Nutzern in Kontakt zu treten, die diese Ansichten teilen oder ablehnen. Dies kann zu produktiven Diskussionen mit einer Konsensbildung führen oder zu strittigen Auseinandersetzungen über auftretende Meinungsverschiedenheiten. Frühere Arbeiten zu diesem Komplex konzentrierten sich in erster Linie darauf, besondere Fälle von asozialem Verhalten wie Hassrede und "Trolling" zu identifizieren, da diese eine Gefahr für die Gesprächskultur und den Wert einer Community darstellen. Die sind jedoch außergewöhnlich schwerwiegende Phänomene, die keinesfalls bei jeder Kontroverse auftreten die sich aus einfachen Diskussionen, Meinungsverschiedenheiten und themenfremden Inhalten ergeben. All diese Reibungspunkte können auch ganz natürlich in einer Diskussion auftauchen, ohne dass diese gleich den ganzen Gesprächsverlauf gefährden. Diese Dissertation stellt ein Framework für die systematische Analyse von Social-Media Diskussionen vor, die vornehmlich von kontroversen Themen, strittigen Standpunkten und Meinungsverschiedenheiten der teilnehmenden Nutzer geprägt sind. Dazu entwickeln wir ein Feature-Modell, um Schlüsselelemente einer Diskussion zu beschreiben. Dazu zählen der Aktivitätsgrad der Benutzer, die Wichtigkeit der einzelnen Aspekte, die Stimmung, die sie ausdrückt, und das Benutzerfeedback. Zunächst bauen wir unser Feature-Modell so auf, um bei Diskussionen gegensätzlicher politischer Kampagnen auf Twitter die oben genannten Schlüsselelemente zu bestimmen. Der Schwerpunkt liegt dabei auf den sachlichen und emotionalen Aspekten der Themen im Bezug auf die Rollen verschiedener Nutzer. Anschließend erweitern wir unseren Ansatz auf Reddit-Diskussionen und nutzen das Community-Feedback, um einen neuen Begriff der Kontroverse zu definieren und Konversationsarchetypen hervorzuheben, die sich aus Interaktionsmustern ergeben. Wir nutzen unser Feature-Modell, um ein Logistischer Regression Verfahren zu entwickeln, das zukünftige Kontroversen in Reddit-Communities in den Themenbereichen Politik, Weltnachrichten, Sport und persönliche Beziehungen vorhersagen kann. Schlussendlich bietet unser Modell auch die Grundlage für eine Vergleichbarkeit verschiedener Communities im Gesundheitsbereich, auch wenn dort die Themen und die Nutzeraktivität, trotz des gemeinsamen Gesamtfokus, erheblich variieren. In jedem der genannten Themenbereiche gibt unser Framework Erkenntnisgewinne, wie das Verhalten der Nutzer die spezifisch Definition von Kontroversen der Community prägt