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International Conference on Computational Data and Social Networks

CSoNet 2022: Computational Data and Social Networks pp 15–26Cite as

Driving Factors of Polarization on Twitter During Protests Against COVID-19 Mitigation Measures in Vienna

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13831))

Abstract

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.

M. Röckl and M. Paul—These authors contributed equally.

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Notes

  1. 1.

    https://www.tagesschau.de/ausland/proteste-wien-103.html. Accessed on 11.11.2022.

  2. 2.

    https://wien.orf.at/stories/3138745/. Accessed on 11.11.2022.

  3. 3.

    https://fasttext.cc/docs/en/crawl-vectors.html. Accessed on 11.11.2022.

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Acknowledgement

This study was partially funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation, project number 458528774).

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Authors and Affiliations

  1. Freie Universität Berlin, Berlin, Germany

    Marcus Röckl & Maximilian Paul

  2. Interdisciplinary Research Institute, Wroclaw, Poland

    Andrzej Jarynowski

  3. System Modeling Group, Institute for Veterinary Epidemiology and Biostatistics, Freie Universität Berlin, Berlin, Germany

    Andrzej Jarynowski & Vitaly Belik

  4. Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, USA

    Alexander Semenov

Authors
  1. Marcus Röckl
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  2. Maximilian Paul
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  3. Andrzej Jarynowski
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  4. Alexander Semenov
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  5. Vitaly Belik
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Corresponding author

Correspondence to Andrzej Jarynowski .

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Editors and Affiliations

  1. Virginia Commonwealth University, Richmond, VA, USA

    Thang N. Dinh

  2. Yeung Kin Man Academic Building, City University Hong Kong, Kowloon Tong, Hong Kong

    Minming Li

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Röckl, M., Paul, M., Jarynowski, A., Semenov, A., Belik, V. (2023). Driving Factors of Polarization on Twitter During Protests Against COVID-19 Mitigation Measures in Vienna. In: Dinh, T.N., Li, M. (eds) Computational Data and Social Networks . CSoNet 2022. Lecture Notes in Computer Science, vol 13831. Springer, Cham. https://doi.org/10.1007/978-3-031-26303-3_2

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  • DOI: https://doi.org/10.1007/978-3-031-26303-3_2

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  • Online ISBN: 978-3-031-26303-3

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