Dancing to the Partisan Beat: A First Analysis of Political Communication on TikTok

TikTok is a video-sharing social networking service, whose popularity is increasing rapidly. It was the world's second-most downloaded app in 2019. Although the platform is known for having users posting videos of themselves dancing, lip-syncing, or showcasing other talents, user-videos expressing political views have seen a recent spurt. This study aims to perform a primary evaluation of political communication on TikTok. We collect a set of US partisan Republican and Democratic videos to investigate how users communicated with each other about political issues. With the help of computer vision, natural language processing, and statistical tools, we illustrate that political communication on TikTok is much more interactive in comparison to other social media platforms, with users combining multiple information channels to spread their messages. We show that political communication takes place in the form of communication trees since users generate branches of responses to existing content. In terms of user demographics, we find that users belonging to both the US parties are young and behave similarly on the platform. However, Republican users generated more political content and their videos received more responses; on the other hand, Democratic users engaged significantly more in cross-partisan discussions.Comment: Accepted as a full paper at the 12th International ACM Web Science Conference (WebSci 2020). Please cite the WebSci version; Second version includes corrected typo

Understanding (Ir)rational Herding Online

Investigations of social influence in collective decision-making have become possible due to recent technologies and platforms that record interactions in far larger groups than could be studied before. Herding and its impact on decision-making are critical areas of practical interest and research study. However, despite theoretical work suggesting that it matters whether individuals choose who to imitate based on cues such as experience or whether they herd at random, there is little empirical analysis of this distinction. To demonstrate the distinction between what the literature calls "rational" and "irrational" herding, we use data on tens of thousands of loans from a well-established online peer-to-peer (p2p) lending platform. First, we employ an empirical measure of memory in complex systems to measure herding in lending. Then, we illustrate a network-based approach to visualize herding. Finally, we model the impact of herding on collective outcomes. Our study reveals that loan performance is not solely determined by whether the lenders engage in herding or not. Instead, the interplay between herding and the imitated lenders' prior success on the platform predicts loan outcomes. In short, herds led by expert lenders tend to pick loans that do not default. We discuss the implications of this under-explored aspect of herding for platform designers, borrowers, and lenders. Our study advances collective intelligence theories based on a case of high-stakes group decision-making online

Online Engagement with Retracted Articles: Who, When, and How?

Retracted research discussed on social media can spread misinformation. Yet we lack an understanding of how retracted articles are mentioned by academic and non-academic users. This is especially relevant on Twitter due to the platform's prominent role in science communication. Here, we analyze the pre- and post-retraction differences in Twitter attention and engagement metrics for over 3,800 retracted English-language articles alongside comparable non-retracted articles. We subset these findings according to five user types detected by our supervised learning classifier: members of the public, academics, bots, science practitioners, and science communicators. We find that retracted articles receive greater user attention (tweet count) and engagement (likes, retweets, and replies) than non-retracted articles, especially among members of the public and bots, with the majority of user engagement happening before retraction. Our results highlight the prominent role of non-experts in discussions of retracted research and suggest an opportunity for social media platforms to contribute towards early detection of problematic scientific research online

Data Quality of Digital Process Data: A Generalized Framework and Simulation/Post-Hoc Identification Strategy

Digital process data are becoming increasingly important for social science research, but their quality has been gravely neglected so far. In this article, we adopt a process perspective and argue that data extracted from socio-technical systems are, in principle, subject to the same error-inducing mechanisms as traditional forms of social science data, namely biases that arise before their acquisition (observational design), during their acquisition (data generation), and after their acquisition (data processing). As the lack of access and insight into the actual processes of data production renders key traditional mechanisms of quality assurance largely impossible, it is essential to identify data quality problems in the data available—that is, to focus on the possibilities post-hoc quality assessment offers to us. We advance a post-hoc strategy of data quality assurance, integrating simulation and explorative identification techniques. As a use case, we illustrate this approach with the example of bot activity and the effects this phenomenon can have on digital process data. First, we employ agent-based modelling to simulate datasets containing these data problems. Subsequently, we demonstrate the possibilities and challenges of post-hoc control by mobilizing geometric data analysis, an exemplary technique for identifying data quality issues.Digitale Prozessdaten werden für die sozialwissenschaftliche Forschung immer wichtiger, doch ihre Qualität wurde in der Diskussion bisher stark vernachlässigt. In diesem Beitrag nehmen wir eine Prozessperspektive ein und argumentieren, dass Daten, die aus soziotechnischen Systemen extrahiert werden, im Prinzip denselben fehlerverursachenden Mechanismen unterliegen wie traditionelle Formen sozialwissenschaftlicher Daten, nämlich Verzerrungen, die vor ihrer Erfassung (Beobachtungsdesign), während ihrer Erfassung (Datengenerierung) und nach ihrer Erfassung (Datenverarbeitung) entstehen. Da der fehlende Zugang und Einblick in die eigentlichen Prozesse der Datenproduktion wichtige Mechanismen der traditionellen Qualitätssicherung weitgehend unmöglich machen, ist es unerlässlich, Datenqualitätsprobleme in den zur Verfügung stehenden Daten zu identifizieren – das heißt, sich auf die Möglichkeiten zu konzentrieren, die uns die post-hoc Qualitätsprüfung bietet. Wir entwickeln eine Post-hoc-Strategie der Datenqualitätssicherung, die Simulation und explorative Identifizierungstechniken integriert. Als Anwendungsfall illustrieren wir diesen Ansatz am Beispiel von Bot-Aktivitäten und den Auswirkungen, die dieses Phänomen auf digitale Prozessdaten haben kann. Dazu setzen wir zunächst eine agentenbasierte Modellierung ein, um Datensätze mit derartigen Datenproblemen zu simulieren. Anschließend demonstrieren wir die Möglichkeiten und Herausforderungen der Post-hoc-Kontrolle, indem wir die geometrische Datenanalyse einsetzen, eine exemplarische Technik zur Identifizierung von Datenqualitätsproblemen

An Agent-based Model to Evaluate Interventions on Online Dating Platforms to Decrease Racial Homogamy

Perhaps the most controversial questions in the study of online platforms today surround the extent to which platforms can intervene to reduce the societal ills perpetrated on them. Up for debate is whether there exist any effective and lasting interventions a platform can adopt to address, e.g., online bullying, or if other, more far-reaching change is necessary to address such problems. Empirical work is critical to addressing such questions. But it is also challenging, because it is time-consuming, expensive, and sometimes limited to the questions companies are willing to ask. To help focus and inform this empirical work, we here propose an agent-based modeling (ABM) approach. As an application, we analyze the impact of a set of interventions on a simulated online dating platform on the lack of long-term interracial relationships in an artificial society. In the real world, a lack of interracial relationships are a critical vehicle through which inequality is maintained. Our work shows that many previously hypothesized interventions online dating platforms could take to increase the number of interracial relationships from their website have limited effects, and that the effectiveness of any intervention is subject to assumptions about sociocultural structure. Further, interventions that are effective in increasing diversity in long-term relationships are at odds with platforms' profit-oriented goals. At a general level, the present work shows the value of using an ABM approach to help understand the potential effects and side effects of different interventions that a platform could take

Leveraging New Technologies and Interdisciplinarity to Study Political Behavior, Attitudes, and Beliefs

I make use of new technological and scholarly developments to study political sentiments and behavior in three independent papers. In my lead paper, I address an important consequence of political deepfakes (i.e., computer-manipulated video misinformation): does the provision of information about deepfakes cause people to disbelieve real political videos? Through a set of online survey experiments, I find that information that is typical of news coverage of deepfakes induces people to disbelieve real political information. My second paper uses new social media datasets to address pressing questions about how organized American far-right groups (e.g., neo-Nazis, white supremacists, etc.) recruit new members, and whether the rise of Trump was used as a catalyst in far-right recruitment efforts. I made use of prior sociological and anthropological research that found that far-right music scenes (featuring bands with such names as Aryan Terrorism) are a key part of day-to-day functioning of the overwhelming majority of far-right hate groups in the United States. As such, I made use of public databases of song listenership on the music social network, Last.fm, before and after Trump events. I find that online friends of frequent listeners of hate music were more likely to increase their levels of hate music listenership after Trump-related events (e.g., xenophobic tweets, primary election victories, etc.). Finally, in my third paper, I leverage new theoretical frameworks in the cognitive sciences and the growth of large-scale, data-driven voter mobilization programs among non-profit organizations to address the puzzle of “voting habits.” Namely, prior research provides strong empirical evidence that voting in one election makes the average individual more likely to vote in a subsequent election, but this kind of turnout persistence does not comport with habit as it is defined in psychological sciences (elections happen too infrequently and voting is never an automatic behavior). So, in my third paper, I apply Duckworth and Gross’s (2020) Process Model of Behavior Change to turnout persistence to bridge the gap between classic economic models of voter turnout and the large body of rigorous empirical evidence showing turnout persistence. I evaluate the concrete predictions made by this model in a novel dataset of ~1.8 million voters across 9 different independent experiments

On the Promotion of the Social Web Intelligence

Given the ever-growing information generated through various online social outlets, analytical research on social media has intensified in the past few years from all walks of life. In particular, works on social Web intelligence foster and benefit from the wisdom of the crowds and attempt to derive actionable information from such data. In the form of collective intelligence, crowds gather together and contribute to solving problems that may be difficult or impossible to solve by individuals and single computers. In addition, the consumer insight revealed from social footprints can be leveraged to build powerful business intelligence tools, enabling efficient and effective decision-making processes. This dissertation is broadly concerned with the intelligence that can emerge from the social Web platforms. In particular, the two phenomena of social privacy and online persuasion are identified as the two pillars of the social Web intelligence, studying which is essential in the promotion and advancement of both collective and business intelligence. The first part of the dissertation is focused on the phenomenon of social privacy. This work is mainly motivated by the privacy dichotomy problem. Users often face difficulties specifying privacy policies that are consistent with their actual privacy concerns and attitudes. As such, before making use of social data, it is imperative to employ multiple safeguards beyond the current privacy settings of users. As a possible solution, we utilize user social footprints to detect their privacy preferences automatically. An unsupervised collaborative filtering approach is proposed to characterize the attributes of publicly available accounts that are intended to be private. Unlike the majority of earlier studies, a variety of social data types is taken into account, including the social context, the published content, as well as the profile attributes of users. Our approach can provide support in making an informed decision whether to exploit one\u27s publicly available data to draw intelligence. With the aim of gaining insight into the strategies behind online persuasion, the second part of the dissertation studies written comments in online deliberations. Specifically, we explore different dimensions of the language, the temporal aspects of the communication, as well as the attributes of the participating users to understand what makes people change their beliefs. In addition, we investigate the factors that are perceived to be the reasons behind persuasion by the users. We link our findings to traditional persuasion research, hoping to uncover when and how they apply to online persuasion. A set of rhetorical relations is known to be of importance in persuasive discourse. We further study the automatic identification and disambiguation of such rhetorical relations, aiming to take a step closer towards automatic analysis of online persuasion. Finally, a small proof of concept tool is presented, showing the value of our persuasion and rhetoric studies