Uncovering Relationships using Bayesian Networks: A Case Study on Conspiracy Theories

Abstract

Bayesian networks (BNs) represent a probabilistic model that can visualize relationships between variables. We apply various BN structure learning algorithms to a large dataset from a Czech university entrance exam. This dataset includes a test of active, open-minded thinking designed by Jonathan Baron, as well as a test of students' attitudes toward various conspiracies. Using BNs, we were able to identify the structure of the conspiracies and their relationships with active open-minded thinking. We also compared results of different BN structure learning algorithms with results of selected standard data analysis methods.