Hunting Conspiracy Theories During the Covid-19 Pandemic

Bibliographic Data

ID2414347
AuthorsJ D Moffitt (0000-0001-6477-8338, Carnegie Mellon University, corresponding author), Catherine King (0000-0002-1636-9887, Carnegie Mellon University), K M Carley (0000-0002-6356-0238, Carnegie Mellon University)
Year2021
Volume7
Issue3
Publication date2021-07-01
Open AccessYes
TypeARTICLE
VenueSocial Media + Society (JOURNAL)
Journal identifiersISSN: 2056-3051 • E-ISSN: 2056-3051
PublisherSAGE Publications Inc (PUBLISHER)
DOI10.1177/20563051211043212
OpenAlexW3198280184
LanguageEN
Citations received17
References cited32

The fear of the unknown combined with the isolation generated by COVID-19 has created a fertile environment for strong disinformation, otherwise known as conspiracy theories, to flourish. Because conspiracy theories often contain a kernel of truth and feature a strong adversarial 'other,' they serve as the perfect vehicle for maligned actors to use in influence campaigns. To explore the importance of conspiracies in the spread of dis-/mis-information, we propose the usage of state-of-the-art, tuned language models to classify tweets as conspiratorial or not. This model is based on the Bidirectional Encoder Representations from Transformers (BERT) model developed by Google researchers. The classification method expedites analysis by automating a process that is currently done manually (identifying tweets that promote conspiracy theories). We identified COVID-19 origin conspiracy theory tweets using this method and then used social cybersecurity methods to analyze communities, spreaders, and characteristics of the different origin-related conspiracy theory narratives. We found that tweets about conspiracy theories were supported by news sites with low fact-checking scores and amplified by bots who were more likely to link to prominent Twitter users than in non-conspiracy tweets. We also found different patterns in conspiracy vs. non-conspiracy conversations in terms of hashtag usage, identity, and country of origin. This analysis shows how we can better understand who spreads conspiracy theories and how they are spreading them

Disinformation · Fake news · Internet privacy · Linguistics · Narrative · Social media · World Wide Web · Computer Science · Hate Speech and Cyberbullying Detection · Misinformation and Its Impacts · Spam and Phishing Detection · Artificial Intelligence

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