Publikationsdatenbank

Driving factors of polarization on Twitter during protests against COVID-19 mitigation measures in Vienna (2023)

Art

Buchbeitrag

Autoren

Röckl, Marcus

Paul, Maximilian

Jarynowski, Andrzej (WE 16)

Semenov, Alexander

Belik, Vitaly (WE 16)

Quelle

Computational Data and Social Networks

1 Auflage

Cham: Springer, 2023. Lecture Notes in Computer Science Series ; v.13831 — S. 15–26

ISBN: 978-3-031-26302-6

Sprache

Englisch

Verweise

URL (Volltext): https://link.springer.com/chapter/10.1007/978-3-031-26303-3_2

DOI: 10.1007/978-3-031-26303-3

Kontakt

Institut für Veterinär-Epidemiologie und Biometrie

Königsweg 67
14163 Berlin
+49 30 838 56034
epi@vetmed.fu-berlin.de

Abstract / Zusammenfassung

We conduct the analysis of the Twitter discourse related to the anti-lockdown and anti-vaccination protests during the so-called 4th wave of COVID-19 infections in Austria (particularly in Vienna). We focus on predicting users’ protest activity by leveraging machine learning methods and individual driving factors such as language features of users supporting/opposing Corona protests. For evaluation of our methods we utilize novel datasets, collected from discussions about a series of protests on Twitter (40488 tweets related to 20.11.2021; 7639 from 15.01.2022 – the two biggest protests as well as 192 from 22.01.2022; 8412 from 11.12.2021; 3945 from 11.02.2022). We clustered users via the Louvain community detection algorithm on a retweet network into pro- and anti-protest classes. We show that the number of users engaged in the discourse and the share of users classified as pro-protest are decreasing with time. We have created language-based classifiers for single tweets of the two protest sides – random forest, neural networks and a regression-based approach. To gain insights into language-related differences between clusters we also investigated variable importance for a word-list-based modeling approach.