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Conspiracy theories on Twitter: emerging motifs and temporal dynamics during the COVID-19 pandemic

[Zeitschriftenartikel]

Batzdorfer, Veronika
Steinmetz, Holger
Biella, Marco
Alizadeh, Meysam

Abstract

The COVID-19 pandemic resulted in an upsurge in the spread of diverse conspiracy theories (CTs) with real-life impact. However, the dynamics of user engagement remain under-researched. In the present study, we leverage Twitter data across 11 months in 2020 from the timelines of 109 CT posters and a ... mehr

The COVID-19 pandemic resulted in an upsurge in the spread of diverse conspiracy theories (CTs) with real-life impact. However, the dynamics of user engagement remain under-researched. In the present study, we leverage Twitter data across 11 months in 2020 from the timelines of 109 CT posters and a comparison group (non-CT group) of equal size. Within this approach, we used word embeddings to distinguish non-CT content from CT-related content as well as analysed which element of CT content emerged in the pandemic. Subsequently, we applied time series analyses on the aggregate and individual level to investigate whether there is a difference between CT posters and non-CT posters in non-CT tweets as well as the temporal dynamics of CT tweets. In this regard, we provide a description of the aggregate and individual series, conducted a STL decomposition in trends, seasons, and errors, as well as an autocorrelation analysis, and applied generalised additive mixed models to analyse nonlinear trends and their differences across users. The narrative motifs, characterised by word embeddings, address pandemic-specific motifs alongside broader motifs and can be related to several psychological needs (epistemic, existential, or social). Overall, the comparison of the CT group and non-CT group showed a substantially higher level of overall COVID-19-related tweets in the non-CT group and higher level of random fluctuations. Focussing on conspiracy tweets, we found a slight positive trend but, more importantly, an increase in users in 2020. Moreover, the aggregate series of CT content revealed two breaks in 2020 and a significant albeit weak positive trend since June. On the individual level, the series showed strong differences in temporal dynamics and a high degree of randomness and day-specific sensitivity. The results stress the importance of Twitter as a means of communication during the pandemic and illustrate that these beliefs travel very fast and are quickly endorsed.... weniger

Thesaurusschlagwörter
Soziale Medien; Twitter; Desinformation; Falschmeldung

Klassifikation
interaktive, elektronische Medien
politische Willensbildung, politische Soziologie, politische Kultur

Freie Schlagwörter
COVID-19; Conspiracy beliefs; Regular Paper; Time series analysis; Twitter structural break analysis; Word embedding

Sprache Dokument
Englisch

Publikationsjahr
2022

Seitenangabe
S. 315-333

Zeitschriftentitel
International Journal of Data Science and Analytics, 13 (2022) 4

Heftthema
Online Information Disorder: Fake News, Bots, and Trolls

DOI
https://doi.org/10.1007/s41060-021-00298-6

ISSN
2364-4168

Status
Veröffentlichungsversion; begutachtet (peer reviewed)

Lizenz
Creative Commons - Namensnennung 4.0


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Home  |  Impressum  |  Betriebskonzept  |  Datenschutzerklärung
© 2007 - 2025 Social Science Open Access Repository (SSOAR).
Based on DSpace, Copyright (c) 2002-2022, DuraSpace. All rights reserved.