Leveraging artificial intelligence to identify the psychological factors associated with conspiracy theory beliefs online

Jonas R. Kunst (), Aleksander B. Gundersen, Izabela Krysińska, Jan Piasecki, Tomi Wójtowicz, Rafal Rygula, Sander van der Linden and Mikolaj Morzy
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Jonas R. Kunst: University of Oslo
Aleksander B. Gundersen: University of Oslo
Izabela Krysińska: Poznan University of Technology
Jan Piasecki: Jagiellonian University Medical College
Tomi Wójtowicz: Poznan University of Technology
Rafal Rygula: Polish Academy of Sciences
Sander van der Linden: University of Cambridge
Mikolaj Morzy: Poznan University of Technology

Nature Communications, 2024, vol. 15, issue 1, 1-17

Abstract: Abstract Given the profound societal impact of conspiracy theories, probing the psychological factors associated with their spread is paramount. Most research lacks large-scale behavioral outcomes, leaving factors related to actual online support for conspiracy theories uncertain. We bridge this gap by combining the psychological self-reports of 2506 Twitter (currently X) users with machine-learning classification of whether the textual data from their 7.7 million social media engagements throughout the pandemic supported six common COVID-19 conspiracy theories. We assess demographic factors, political alignment, factors derived from theory of reasoned action, and individual psychological differences. Here, we show that being older, self-identifying as very left or right on the political spectrum, and believing in false information constitute the most consistent risk factors; denialist tendencies, confidence in one’s ability to spot misinformation, and political conservativism are positively associated with support for one conspiracy theory. Combining artificial intelligence analyses of big behavioral data with self-report surveys can effectively identify and validate risk factors for phenomena evident in large-scale online behaviors.

Date: 2024
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DOI: 10.1038/s41467-024-51740-9

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