National Predictors of Conspiracy Beliefs: A Machine Learning Analysis of Cross-Country Data

Résumé fourni par la source

Conspiracy beliefs vary widely across nations, yet their societal predictors remain poorly understood. Applying machine-learning meta-analytic techniques, we simultaneously examined 18 national-level developmental and cultural factors and ranked their relative importance in predicting conspiracy beliefs. Across six large cross-national datasets (total N = 178,789; 72 countries), the best-performing model (MetaForest) explained 43% of variance in test data. Corruption was the strongest predictor, followed by power distance, reading proficiency, individualism, mathematics proficiency, human development, GDP, and science proficiency. Conspiracy beliefs were most prevalent in societies marked by higher corruption and power distance, and lower educational performance, individualism, and socioeconomic development; developmental indicators generally outranked cultural ones, and dimensions such as WEIRDness, uncertainty avoidance, and long-term orientation mattered little. By integrating developmental and cultural predictors in a single framework, this study offers the most comprehensive test to date of national correlates of conspiracy beliefs and demonstrates the value of machine-learning approaches.

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