Douglas, K.M. orcid.org/0000-0002-0381-6924, Sutton, R.M. orcid.org/0000-0003-1542-1716
, Van Lissa, C.J. et al. (99 more authors)
(2023)
Identifying important individual‐ and country‐level predictors of conspiracy theorizing: a machine learning analysis.
European Journal of Social Psychology, 53 (6).
pp. 1191-1203.
ISSN 0046-2772
Abstract
Psychological research on the predictors of conspiracy theorizing—explaining important social and political events or circumstances as secret plots by malevolent groups—has flourished in recent years. However, research has typically examined only a small number of predictors in one, or a small number of, national contexts. Such approaches make it difficult to examine the relative importance of predictors, and risk overlooking some potentially relevant variables altogether. To overcome this limitation, the present study used machine learning to rank-order the importance of 115 individual- and country-level variables in predicting conspiracy theorizing. Data were collected from 56,072 respondents across 28 countries during the early weeks of the COVID-19 pandemic. Echoing previous findings, important predictors at the individual level included societal discontent, paranoia, and personal struggle. Contrary to prior research, important country-level predictors included indicators of political stability and effective government COVID response, which suggests that conspiracy theorizing may thrive in relatively well-functioning democracies.
Metadata
Item Type: | Article |
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Authors/Creators: | This paper has 102 authors. You can scroll the list below to see them all or them all.
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Copyright, Publisher and Additional Information: | © 2023 The Authors. European Journal of Social Psychology published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited (https://creativecommons.org/licenses/by/4.0/). |
Keywords: | conspiracy theories; country-level variables; COVID-19; machine learning; individual-level variables |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Science (Sheffield) > Department of Psychology (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 Jul 2023 12:39 |
Last Modified: | 04 Oct 2024 11:18 |
Status: | Published |
Publisher: | Wiley |
Refereed: | Yes |
Identification Number: | 10.1002/ejsp.2968 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:201150 |