Going beyond simplicity

• Verfasst von: Brandenstein, Nils [VerfasserIn]
• Titel: Going beyond simplicity
• Titelzusatz: using machine learning to predict belief in conspiracy theories
• Verf.angabe: Nils Brandenstein
• E-Jahr: 2022
• Jahr: 03 October 2022
• Umfang: 21 S.
• Illustrationen: Diagramme
• Fussnoten: Gesehen am 21.12.2022
• Titel Quelle: Enthalten in: European journal of social psychology
• Ort Quelle: Malden, Mass. : Wiley-Blackwell, 1971
• Jahr Quelle: 2022
• Band/Heft Quelle: 52(2022), 5/6 vom: Okt., Seite 910-930
• ISSN Quelle: 1099-0992
• DOI: doi:10.1002/ejsp.2859
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: belief
• Sach-SW: conspiracy
• Sach-SW: machine learning
• Sach-SW: political attitudes
• Sach-SW: prediction
• Sach-SW: social psychology
• K10plus-PPN: 182824192X
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

Public and scientific interest in why people believe in conspiracy theories (CT) surged in the past years. To come up with a theoretical explanation, researchers investigated relationships of CT belief with psychological factors such as political attitudes, emotions, or personality. However, recent studies have put the robustness of these relationships into question. In the present study, a prediction-based analysis approach and machine learning models are deployed to detect and remedy poor replicability of CT belief associations. The analysis of a representative dataset with 2025 UK citizens supports the assumption that the current simplicity of the field's analysis routine, exhibiting high sample-specificity and neglecting complex associations of psychological factors with CT belief, may obscure important relationships. The results further point towards key components of conspiratorial mindsets like general distrust and low socio-political control. Important implications for building a coherent theory of CT belief are derived.