Going beyond simplicity: Using machine learning to predict belief in conspiracy theories

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 (van Prooijen & Douglas, 2018). However, recent studies put the robustness of these relationships into question (e.g., Stojanov & Halberstadt, 2020). In this study, the analysis of a representative dataset with 2025 adults uncovered that the simplicity of the current analysis routine, exhibiting high sample-specificity and neglecting complex associations of psychological factors and belief in CTs, may obscure these relationships. Further, poor replicability of CT belief associations can be detected and remedied by using a prediction-based modeling approach and machine learning models, which proposes a timely shift in the field’s analysis routine. Conceptual and theoretical implications for CT belief research and theory building are derived.