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Propagating and Debunking Conspiracy Theories on Twitter During the 2015-2016 Zika Virus Outbreak.

ABSTRACT: The present study investigates the characteristics of discussion of conspiracy theories about the Zika virus outbreak of 2015-16 on Twitter. Content and social network analysis of a dataset of 25,162 original Tweets about Zika virus conspiracy theories showed that relative to debunking messages, conspiracy theories spread through a more decentralized network, are more likely to invoke supposedly knowledgeable authorities in making arguments, and ask more rhetorical questions. These trends can be understood in the context of previous work on conspiracy theories, including the "just asking questions" style of rhetoric, the importance of sourcing and authority, and the tendency to simultaneously consider many different potential conspiracies that might underlie an important topic or event.

SUBMITTER: Wood MJ

PROVIDER: S-EPMC6094351 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Propagating and Debunking Conspiracy Theories on Twitter During the 2015-2016 Zika Virus Outbreak.

Wood Michael J MJ

Cyberpsychology, behavior and social networking 20180718 8

The present study investigates the characteristics of discussion of conspiracy theories about the Zika virus outbreak of 2015-16 on Twitter. Content and social network analysis of a dataset of 25,162 original Tweets about Zika virus conspiracy theories showed that relative to debunking messages, conspiracy theories spread through a more decentralized network, are more likely to invoke supposedly knowledgeable authorities in making arguments, and ask more rhetorical questions. These trends can be ...[more]

PMID: 30020821

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Project description: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.Supplementary informationThe online version contains supplementary material available at 10.1007/s41060-021-00298-6.

Dataset Information

Propagating and Debunking Conspiracy Theories on Twitter During the 2015-2016 Zika Virus Outbreak.

Publications

Propagating and Debunking Conspiracy Theories on Twitter During the 2015-2016 Zika Virus Outbreak.

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