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.
Cite
Text
Vomlel et al. "Uncovering Relationships Using Bayesian Networks: A Case Study on Conspiracy Theories." Proceedings of The 12th International Conference on Probabilistic Graphical Models, 2024.Markdown
[Vomlel et al. "Uncovering Relationships Using Bayesian Networks: A Case Study on Conspiracy Theories." Proceedings of The 12th International Conference on Probabilistic Graphical Models, 2024.](https://mlanthology.org/pgm/2024/vomlel2024pgm-uncovering/)BibTeX
@inproceedings{vomlel2024pgm-uncovering,
title = {{Uncovering Relationships Using Bayesian Networks: A Case Study on Conspiracy Theories}},
author = {Vomlel, Jiřı́ and Kuběna, Aleš and Šmı́d, Martin and Weinerova, Josefina},
booktitle = {Proceedings of The 12th International Conference on Probabilistic Graphical Models},
year = {2024},
pages = {470-485},
volume = {246},
url = {https://mlanthology.org/pgm/2024/vomlel2024pgm-uncovering/}
}