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

Jiřı́ Vomlel, Aleš Kuběna, Martin Šmı́d, Josefina Weinerova
Proceedings of The 12th International Conference on Probabilistic Graphical Models, PMLR 246:470-485, 2024.

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 this Paper

BibTeX
@InProceedings{pmlr-v246-vomlel24a, title = {Uncovering Relationships using Bayesian Networks: A Case Study on Conspiracy Theories}, author = {Vomlel, Ji\v{r}\'{\i} and Kub\v{e}na, Ale\v{s} and \v{S}m\'{\i}d, Martin and Weinerova, Josefina}, booktitle = {Proceedings of The 12th International Conference on Probabilistic Graphical Models}, pages = {470--485}, year = {2024}, editor = {Kwisthout, Johan and Renooij, Silja}, volume = {246}, series = {Proceedings of Machine Learning Research}, month = {11--13 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v246/main/assets/vomlel24a/vomlel24a.pdf}, url = {https://proceedings.mlr.press/v246/vomlel24a.html}, 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.} }
Endnote
%0 Conference Paper %T Uncovering Relationships using Bayesian Networks: A Case Study on Conspiracy Theories %A Jiřı́ Vomlel %A Aleš Kuběna %A Martin Šmı́d %A Josefina Weinerova %B Proceedings of The 12th International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2024 %E Johan Kwisthout %E Silja Renooij %F pmlr-v246-vomlel24a %I PMLR %P 470--485 %U https://proceedings.mlr.press/v246/vomlel24a.html %V 246 %X 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.
APA
Vomlel, J., Kuběna, A., Šmı́d, M. & Weinerova, J.. (2024). Uncovering Relationships using Bayesian Networks: A Case Study on Conspiracy Theories. Proceedings of The 12th International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 246:470-485 Available from https://proceedings.mlr.press/v246/vomlel24a.html.

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