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

[journal article]

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 ... view more

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.... view less

Keywords
social media; twitter; disinformation; false report

Classification
Interactive, electronic Media
Political Process, Elections, Political Sociology, Political Culture

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

Document language
English

Publication Year
2022

Page/Pages
p. 315-333

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

Issue topic
Online Information Disorder: Fake News, Bots, and Trolls

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

ISSN
2364-4168

Status
Published Version; peer reviewed

Licence
Creative Commons - Attribution 4.0


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Home  |  Legal notices  |  Operational concept  |  Privacy policy
© 2007 - 2025 Social Science Open Access Repository (SSOAR).
Based on DSpace, Copyright (c) 2002-2022, DuraSpace. All rights reserved.