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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F24%3A00598454" target="_blank" >RIV/67985556:_____/24:00598454 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11210/24:10486228
Result on the web
<a href="https://proceedings.mlr.press/v246/vomlel24a.html" target="_blank" >https://proceedings.mlr.press/v246/vomlel24a.html</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Uncovering Relationships using Bayesian Networks: A Case Study on Conspiracy Theories
Original language description
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.
Czech name
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Czech description
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Project
<a href="/en/project/EH22_008%2F0004595" target="_blank" >EH22_008/0004595: Beyond Security: Role of Conflict in Resilience-Building</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of Machine Learning Research (PMLR), Volume 246 : International Conference on Probabilistic Graphical Models
ISBN
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ISSN
2640-3498
e-ISSN
2640-3498
Number of pages
16
Pages from-to
470-485
Publisher name
JMLR-JOURNAL MACHINE LEARNING RESEARCH
Place of publication
San Diego
Event location
Nijmegen
Event date
Sep 11, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001347210900028

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