{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T20:55:44Z","timestamp":1778878544440,"version":"3.51.4"},"reference-count":36,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T00:00:00Z","timestamp":1740528000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The spread of health-related misinformation has become a significant global challenge, particularly during the COVID-19 pandemic. This study introduces a comprehensive framework for detecting and analyzing misinformation using advanced natural language processing techniques. The proposed classification model combines BERT embeddings with Bi-LSTM architecture and attention mechanisms, achieving high performance, including 99.47% accuracy and an F1-score of 0.9947. In addition to classification, topic modeling is employed to identify thematic clusters, providing valuable insights into misinformation narratives. The findings demonstrate the effectiveness and reliability of the proposed methodology in detecting misinformation while offering tools for understanding its underlying themes. The adaptable and scalable approach makes it applicable to various domains and datasets. This research improves public health communication and combating misinformation in digital environments.<\/jats:p>","DOI":"10.3390\/info16030175","type":"journal-article","created":{"date-parts":[[2025,2,26]],"date-time":"2025-02-26T04:46:37Z","timestamp":1740545197000},"page":"175","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Novel Comprehensive Framework for Detecting and Understanding Health-Related Misinformation"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6014-1065","authenticated-orcid":false,"given":"Halyna","family":"Padalko","sequence":"first","affiliation":[{"name":"Mathematical Modelling and Artificial Intelligence Department, National Aerospace University \u201cKharkiv Aviation Institute\u201d, 61070 Kharkiv, Ukraine"},{"name":"Political Science Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada"},{"name":"Balsillie School of International Affairs, Waterloo, ON N2L 6G2, Canada"},{"name":"Center for International Governance Innovation, Waterloo, ON N2L 6C2, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4419-6651","authenticated-orcid":false,"given":"Vasyl","family":"Chomko","sequence":"additional","affiliation":[{"name":"System Design Engineering Department, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1707-843X","authenticated-orcid":false,"given":"Sergiy","family":"Yakovlev","sequence":"additional","affiliation":[{"name":"Institute of Mathematics, Lodz University of Technology, 90-924 Lodz, Poland"},{"name":"Institute of Computer Science and Artificial Intelligence, V.N. Karazin Kharkiv National University, 61000 Kharkiv, Ukraine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2623-3294","authenticated-orcid":false,"given":"Dmytro","family":"Chumachenko","sequence":"additional","affiliation":[{"name":"Mathematical Modelling and Artificial Intelligence Department, National Aerospace University \u201cKharkiv Aviation Institute\u201d, 61070 Kharkiv, Ukraine"},{"name":"Balsillie School of International Affairs, Waterloo, ON N2L 6G2, Canada"},{"name":"Ubiquitous Health Technology Lab, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,26]]},"reference":[{"key":"ref_1","unstructured":"(2025, January 13). World Health Organization Managing the COVID-19 Infodemic: Promoting Healthy Behaviours and Mitigating the Harm from Misinformation and Disinformation. 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