Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Incorporating Neighborhood Information and Sentence Embedding Similarity into a Repost Prediction Model in Social Media Networks Read chapter Read first chapter

2023 | OriginalPaper | Chapter

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

Authors : Marcus Röckl, Maximilian Paul, Andrzej Jarynowski, Alexander Semenov, Vitaly Belik

Published in: Computational Data and Social Networks

Publisher: Springer Nature Switzerland

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Incorporating Neighborhood Information and Sentence Embedding Similarity into a Repost Prediction Model in Social Media Networks
next chapter Categorizing Memes About the Ukraine Conflict
Footnotes
Literature
1.
2.
Alessa, A., Faezipour, M., Alhassan, Z.: Sentiment and structure in word co-occurrence networks on twitter. In: IEEE International Conference on Healthcare Informatics (ICHI), pp. 366–367 (2018)
3.
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvreo, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008, P10008 (2008)
4.
Boyd, R.L., Ashokkumar, A., Seraj, S., Pennebaker, J.W.: The development and psychometric properties of LIWC-22. In: University of Texas at Austin (2022)
5.
Brunner, M., Daniel, A., Knasmüller, F., Maile, F., Schadauer, A., Stern, V.: Corona-protest-report. Narrative-motive-einstellungen (2021)
6.
Cossard, A., Morales, G.D.F., Kalimeri, K., Mejova, Y., Paolotti, D., Starnini, M.: Falling into the echo chamber: the Italian vaccination debate on Twitter. In: ICWSM (2020)
7.
Di Sia, P.: Current perception of epidemic between traditional and social media: an Italian case study (2022)
8.
Diani, M.: The Cement of Civil Society. Cambridge University Press, Cambridge (2015)CrossRef
9.
Eysenbach, G.: How to fight an infodemic: the four pillars of infodemic management. J. Med. Internet Res. 22(6), e21820 (2020)CrossRef
10.
Frei, N., Schäfer, R., Nachtwey, O.: Die proteste gegen die corona-maßnahmen. Forschungsjournal Soziale Bewegungen 34(2), 249–258 (2021)CrossRef
11.
Fudolig, M., Alshaabi, T., Arnold, M., Danforth, C., Dodds, P.: Sentiment and structure in word co-occurrence networks on Twitter. Appl. Netw. Sci. 7(9), 1–27 (2022)
12.
Gerbaudo, P.: Tweets and the Streets: Social Media and Contemporary Activism. Pluto Press (2012)
13.
Gerbaudo, P.: The pandemic crowd. J. Int. Aff. 73(2), 61–76 (2020)
14.
Grande, E., Hutter, S., Hunger, S., Kanol, E.: Alles covidioten? Politische potenziale des corona-protests in deutschland. Technical report, WZB Discussion Paper (2021)
15.
Hale, T., et al.: A global panel database of pandemic policies (Oxford COVID-19 government response tracker). Nat. Hum. Behav. 5(4), 529–538 (2021)CrossRef
16.
Hoseini, M., Melo, P., Benevenuto, F., Feldmann, A., Zannettou, S.: On the globalization of the QAnon conspiracy theory through telegram. arXiv preprint arXiv:​2105.​13020 (2021)
17.
18.
Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. arXiv preprint arXiv:​1607.​01759 (2016)
19.
Kemmesies, U., et al.: Motra-monitor (2022)
20.
Koos, S.: Die “querdenker”. Wer nimmt an Corona-Protesten teil und warum (2021)
21.
Kowalewski, M.: Street protests in times of COVID-19: adjusting tactics and marching ‘as usual’. Soc. Move. Stud. 20, 1–8 (2020)
22.
Lundberg, S., Lee, S.I.: A unified approach to interpreting model predictions. In: 31st Conference on Neural Information Processing Systems (NIPS) (2017)
23.
24.
Pellert, M., Lasser, J., Metzler, H., Garcia, D.: Dashboard of sentiment in Austrian social media during COVID-19. Front. Big Data 3, 32 (2020)CrossRef
25.
Płatek, D.: Przemoc skrajnej prawicy w polsce. Analiza strategicznego pola ruchu społecznego. Studia Socjologiczne 239, 123–153 (2020)
26.
Pope, D., Griffith, J.: An analysis of online Twitter sentiment surrounding the European refugee crisis. In: KDIR (2016)
27.
Rodak, O.: Hashtag hijacking and crowdsourcing transparency: social media affordances and the governance of farm animal protection. Agric. Hum. Values 37(2), 281–294 (2020)CrossRef
28.
Seiler, M.: From anti-mask to anti-state: anti-lockdown protests, conspiracy thinking and the risk of radicalization (2021)
29.
Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang. Soc. Psychol. 29(1), 24–54 (2010)CrossRef
30.
van der Zwet, K., Barros, A.I., van Engers, T.M., Sloot, P.: Emergence of protests during the COVID-19 pandemic: quantitative models to explore the contributions of societal conditions. Human. Soc. Sci. Commun. 9(1), 1–11 (2022)
Metadata
Title
Driving Factors of Polarization on Twitter During Protests Against COVID-19 Mitigation Measures in Vienna
Authors
Marcus Röckl
Maximilian Paul
Andrzej Jarynowski
Alexander Semenov
Vitaly Belik
Copyright Year
2023
Book
Computational Data and Social Networks

Print ISBN: 978-3-031-26302-6

Electronic ISBN: 978-3-031-26303-3

Copyright Year: 2023

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

Premium Partner

    Image Credits