{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:35:32Z","timestamp":1761176132413,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>The pervasive dissemination of fake news presents significant challenges to societal well-being and informed decision-making, necessitating robust detection mechanisms with calibrated uncertainty measures. This paper proposes a novel hybrid framework for fake news detection, integrating uncertainty quantification with a domain-specific Knowledge Base approach. The BANED knowledge base models word-level probabilistic significance, leveraging statistical support metrics to assess prediction uncertainty. By incorporating these metrics into a Bayesian framework, our method provides well-calibrated predictive distributions, offering enhanced interpretability and robustness in the presence of ambiguous or conflicting news data. The proposed approach is evaluated on the FakeNewsNet and ISOT Fake News datasets, demonstrating competitive accuracy and superior reliability compared to state-of-the-art Bayesian inference techniques. Combining word-level probabilistic significance with Monte Carlo Dropout decreases mean calibration error and narrows the interquartile range of predictions. Full code and supplementary materials of BANED might be found at https:\/\/github.com\/micbizon\/BANED.<\/jats:p>","DOI":"10.3233\/faia250858","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:44:13Z","timestamp":1761126253000},"source":"Crossref","is-referenced-by-count":0,"title":["Knowledge-Driven Bayesian Uncertainty Quantification for Reliable Fake News Detection"],"prefix":"10.3233","author":[{"given":"Julia","family":"Puczy\u0144ska","sequence":"first","affiliation":[{"name":"IDEAS NCBR Sp. z o.o., 69 Chmielna Street, 00-801 Warsaw, Poland"},{"name":"IPPT PAN, Pawi\u0144skiego 5B, 02-106 Warsaw, Poland"}]},{"given":"Youcef","family":"Djenouri","sequence":"additional","affiliation":[{"name":"University of South-Eastern Norway (USN), Post office box 4, 3199 Borre, Norway"},{"name":"Norwegian Research Center (NORCE), Oslo, Norway"}]},{"given":"Micha\u0142","family":"Bizo\u0144","sequence":"additional","affiliation":[{"name":"IDEAS NCBR Sp. z o.o., 69 Chmielna Street, 00-801 Warsaw, Poland"}]},{"given":"Tomasz","family":"Michalak","sequence":"additional","affiliation":[{"name":"Institute of Informatics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland"},{"name":"IDEAS Research Institute, 27 Kr\u00f3lewska, 00-060 Warsaw, Poland"}]},{"given":"Piotr","family":"Sankowski","sequence":"additional","affiliation":[{"name":"Institute of Informatics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland"},{"name":"IDEAS Research Institute, 27 Kr\u00f3lewska, 00-060 Warsaw, Poland"},{"name":"MIM Solutions, 47 \u015awieradowska, 02-662 Warsaw, Poland"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250858","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:44:14Z","timestamp":1761126254000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250858"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250858","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}