Finding Qs: Profiling QAnon Supporters on Parler

Bär, Dominik; Pröllochs, Nicolas und Feuerriegel, Stefan (2022): Finding Qs: Profiling QAnon Supporters on Parler. ICWSM 2023, Limassol, 05.06.2023-08.06.2023. arXiv.

Abstract

The social media platform "Parler" has emerged into a prominent fringe community where a significant part of the user base are self-reported supporters of QAnon, a far-right conspiracy theory alleging that a cabal of elites controls global politics. QAnon is considered to have had an influential role in the public discourse during the 2020 U.S. presidential election. However, little is known about QAnon supporters on Parler and what sets them aside from other users. Building up on social identity theory, we aim at profiling the characteristics of QAnon supporters on Parler. We analyze a large-scale dataset with more than 600,000 profiles of English-speaking users on Parler. Based on users' profiles, posts, and comments, we then extract a comprehensive set of user features, linguistic features, network features, and content features. This allows us to perform user profiling and understand to what extent these features discriminate between QAnon and non-QAnon supporters on Parler. Our analysis is three-fold: (1) We quantify the number of QAnon supporters on Parler, finding that 34,913 users (5.5% of all users) openly report to support the conspiracy. (2) We examine differences between QAnon vs. non-QAnon supporters. We find that QAnon supporters differ statistically significantly from non-QAnon supporters across multiple dimensions. For example, they have, on average, a larger number of followers, followees, and posts, and thus have a large impact on the Parler network. (3) We use machine learning to identify which user characteristics discriminate QAnon from non-QAnon supporters. We find that user features, linguistic features, network features, and content features, can - to a large extent - discriminate QAnon vs. non-QAnon supporters on Parler. In particular, we find that user features are highly discriminatory, followed by content features and linguistic features.

• Dokumententyp: Konferenzbeitrag (Paper)
• Keywords: Social and Information Networks (cs.SI); FOS: Computer and information sciences; FOS: Computer and information sciences; Artificial Intelligence; AI, Künstliche Intelligenz; KI
• Fakultät: Betriebswirtschaft > Institute of Artificial Intelligence (AI) in Management
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 000 Informatik, Wissen, Systeme
• Sprache: Englisch