{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T10:07:00Z","timestamp":1778839620187,"version":"3.51.4"},"reference-count":45,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T00:00:00Z","timestamp":1778716800000},"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 unverified health claims about drugs, dietary supplements, and alternative remedies on social media poses a growing public health concern. In this study, we present a retrieval-augmented generation (RAG) pipeline that uses large language models (LLMs) grounded in biomedical evidence from PubMed, openFDA adverse event reports, and NIH\/NCCIH dietary supplement fact sheets to detect and classify health product misinformation. A total of 3493 health-related posts were collected from Reddit (948 posts across 12 subreddits) and YouTube (2545 video descriptions and comments), from which 8250 structured claims were extracted using Claude Haiku. Each claim was matched to biomedical evidence from three authoritative sources, achieving 79.4% evidence coverage, and classified into one of five veracity categories: supported (7.0%), unsupported (59.9%), exaggerated (22.4%), contradicted (2.0%), or dangerous (8.6%), together with an associated risk tier. Overall, 13.5% of claims were assigned high or critical risk. Cross-platform analysis showed that YouTube contained higher proportions of dangerous (11.3% vs. 2.9%) and exaggerated (27.0% vs. 12.4%) claims than Reddit. Compared with keyword-based and zero-shot transformer baselines, the LLM+RAG pipeline produced a more balanced and fine-grained classification of unsupported, exaggerated, contradicted, and dangerous claims. The most frequently implicated products were ashwagandha, kratom, black seed oil, turmeric, and ivermectin, with disease cure claims showing the highest dangerous classification rate (30.1%). These model-assigned results suggest that evidence-grounded LLM pipelines can support health misinformation surveillance, while also highlighting the need for expert validation and broader cross-platform evaluation.<\/jats:p>","DOI":"10.3390\/info17050481","type":"journal-article","created":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T08:50:12Z","timestamp":1778835012000},"page":"481","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Detecting Health Product Misinformation on Social Media Using Large Language Models Grounded in Biomedical Evidence"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1115-2504","authenticated-orcid":false,"given":"Sara","family":"Behnamian","sequence":"first","affiliation":[{"name":"Globe Institute, University of Copenhagen, 1353 Copenhagen, Denmark"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1520-1799","authenticated-orcid":false,"given":"Zeinab","family":"Shahbazi","sequence":"additional","affiliation":[{"name":"Research Environment of Computer Science (RECS), Kristianstad University, 291 88 Kristianstad, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4093-1156","authenticated-orcid":false,"given":"Zahra","family":"Shahbazi","sequence":"additional","affiliation":[{"name":"Department of Environmental Engineering, University of Padova, 35131 Padova, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9308-1062","authenticated-orcid":false,"given":"Sadiqa","family":"Jafari","sequence":"additional","affiliation":[{"name":"School of Computing, Gachon University, Seongnam 13120, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e17187","DOI":"10.2196\/17187","article-title":"Prevalence of Health Misinformation on Social Media: Systematic Review","volume":"23","year":"2021","journal-title":"J. Med. Internet Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3637405","article-title":"\u201cThe Headline Was So Wild That I Had To Check\u201d: An Exploration of Women\u2019s Encounters with Health Misinformation on Social Media","volume":"8","author":"Malki","year":"2024","journal-title":"Proc. ACM Hum.-Comput. Interact."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e56931","DOI":"10.2196\/56931","article-title":"A Comprehensive Analysis of COVID-19 Misinformation, Public Health Impacts, and Communication Strategies: Scoping Review","volume":"26","author":"Kisa","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"544","DOI":"10.2471\/BLT.21.287654","article-title":"Infodemics and Health Misinformation: A Systematic Review of Reviews","volume":"100","author":"Pizarro","year":"2022","journal-title":"Bull. World Health Organ."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Clark, O., and Joshi, K.P. (2025). Evaluating Causal AI Techniques for Health Misinformation Detection. Proceedings of the 2025 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Workshops), Washington, DC, USA, 17\u201321 March 2025, IEEE.","DOI":"10.1109\/PerComWorkshops65533.2025.00040"},{"key":"ref_6","unstructured":"Alkhawaldeh, F. (2022). False Textual Information Detection, a Deep Learning Approach. [Ph.D. Thesis, University of York]."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Allam, H., Makubvure, L., Gyamfi, B., Graham, K.N., and Akinwolere, K. (2025). Text Classification: How Machine Learning Is Revolutionizing Text Categorization. Information, 16.","DOI":"10.3390\/info16020130"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Mohasseb, A., Amer, E., Chiroma, F., and Tranchese, A. (2025). Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny Detection. Appl. Sci., 15.","DOI":"10.3390\/app15020856"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1073\/pnas.1517441113","article-title":"The Spreading of Misinformation Online","volume":"113","author":"Bessi","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e4","DOI":"10.2196\/jmir.5051","article-title":"Health Advice from Internet Discussion Forums: How Bad Is Dangerous?","volume":"18","author":"Cole","year":"2016","journal-title":"J. Med. Internet Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1146\/annurev-publhealth-040119-094127","article-title":"Public Health and Online Misinformation: Challenges and Recommendations","volume":"41","author":"Lazer","year":"2020","journal-title":"Annu. Rev. Public Health"},{"key":"ref_12","unstructured":"Beres, D., Remski, M., and Walker, J. (2023). Conspirituality: How New Age Conspiracy Theories Became a Health Threat, PublicAffairs."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xie, Q., Schenck, E.J., Yang, H.S., Chen, Y., Peng, Y., and Wang, F. (2023). Faithful AI in Medicine: A Systematic Review with Large Language Models and Beyond. medRxiv, 2023.04.18.23288752.","DOI":"10.21203\/rs.3.rs-3661764\/v1"},{"key":"ref_14","first-page":"1","article-title":"HC-COVID: A Hierarchical Crowdsource Knowledge Graph Approach to Explainable COVID-19 Misinformation Detection","volume":"6","author":"Kou","year":"2022","journal-title":"Proc. ACM Hum.-Comput. Interact."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cui, L., Seo, H., Tabar, M., Ma, F., Wang, S., and Lee, D. (2020). DETERRENT: Knowledge Guided Graph Attention Network for Detecting Healthcare Misinformation. KDD \u201920: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Association for Computing Machinery.","DOI":"10.1145\/3394486.3403092"},{"key":"ref_16","unstructured":"Hu, X., Chen, J., Li, X., Guo, Y., Wen, L., Yu, P.S., and Guo, Z. (2023). Do Large Language Models Know about Facts?. arXiv."},{"key":"ref_17","unstructured":"Zhang, T., Luo, H., Chuang, Y.S., Fang, W., Gaitskell, L., Hartvigsen, T., Wu, X., Fox, D., Meng, H., and Glass, J. (2023). Interpretable Unified Language Checking. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1038\/d41586-023-00288-7","article-title":"ChatGPT: Five Priorities for Research","volume":"614","author":"Bollen","year":"2023","journal-title":"Nature"},{"key":"ref_19","unstructured":"Nori, H., King, N., McKinney, S.M., Carignan, D., and Horvitz, E. (2023). Capabilities of GPT-4 on Medical Challenge Problems. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2991\/nlpr.d.200522.001","article-title":"Motivations, Methods and Metrics of Misinformation Detection: An NLP Perspective","volume":"1","author":"Su","year":"2020","journal-title":"Nat. Lang. Process. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1007\/s11042-023-15364-3","article-title":"Word2Vec and LSTM-Based Deep Learning Technique for Context-Free Fake News Detection","volume":"83","author":"Mallik","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Al-Tarawneh, M.A., Al-irr, O., Al-Maaitah, K.S., Kanj, H., and Aly, W.H.F. (2024). Enhancing Fake News Detection with Word Embedding: A Machine Learning and Deep Learning Approach. Computers, 13.","DOI":"10.20944\/preprints202407.2317.v1"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1118","DOI":"10.1038\/s41598-025-85308-4","article-title":"Tackling Misinformation in Mobile Social Networks: A BERT-LSTM Approach for Enhancing Digital Literacy","volume":"15","author":"Wang","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kukreja, S., Kumar, T., Purohit, A., Dasgupta, A., and Guha, D. (2024). A Literature Survey on Open Source Large Language Models. ICCMB \u201924: Proceedings of the 2024 7th International Conference on Computers in Management and Business, Association for Computing Machinery.","DOI":"10.1145\/3647782.3647803"},{"key":"ref_25","unstructured":"Bai, G., Chai, Z., Ling, C., Wang, S., Lu, J., Zhang, N., Shi, T., Yu, Z., Zhu, M., and Zhang, Y. (2024). Beyond Efficiency: A Systematic Survey of Resource-Efficient Large Language Models. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"103076","DOI":"10.1016\/j.jnca.2021.103076","article-title":"Domain-Specific Knowledge Graphs: A Survey","volume":"185","year":"2021","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3282","DOI":"10.1109\/JBHI.2023.3304361","article-title":"A Comprehensive Privacy-Preserving Federated Learning Scheme with Secure Authentication and Aggregation for Internet of Medical Things","volume":"28","author":"Liu","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.ject.2024.11.001","article-title":"Leveraging Federated Learning for Privacy-Preserving Analysis of Multi-Institutional Electronic Health Records in Rare Disease Research","volume":"3","author":"Meduri","year":"2025","journal-title":"J. Econ. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"83562","DOI":"10.1109\/ACCESS.2023.3301162","article-title":"Advancing Federated Learning through Novel Mechanism for Privacy Preservation in Healthcare Applications","volume":"11","author":"Abaoud","year":"2023","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"53881","DOI":"10.1109\/ACCESS.2024.3388992","article-title":"Secure Multi-Party Computation for Machine Learning: A Survey","volume":"12","author":"Zhou","year":"2024","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1038\/s41746-025-01429-0","article-title":"Privacy-Preserving Strategies for Electronic Health Records in the Era of Large Language Models","volume":"8","author":"Jonnagaddala","year":"2025","journal-title":"npj Digit. Med."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Papageorgiou, E., Chronis, C., Varlamis, I., and Himeur, Y. (2024). A Survey on the Use of Large Language Models (LLMs) in Fake News. Future Internet, 16.","DOI":"10.3390\/fi16080298"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lucas, J., Uchendu, A., Yamashita, M., Lee, J., Rohatgi, S., and Lee, D. (2023). Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics.","DOI":"10.18653\/v1\/2023.emnlp-main.883"},{"key":"ref_34","first-page":"354","article-title":"Combating Misinformation in the Age of LLMs: Opportunities and Challenges","volume":"45","author":"Chen","year":"2024","journal-title":"AI Mag."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Shahbazi, Z., and Behnamian, S. (2026). Using Large Language Models to Detect and Debunk Climate Change Misinformation. Big Data Cogn. Comput., 10.","DOI":"10.3390\/bdcc10010034"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Pendyala, V.S., and Hall, C.E. (2024). Explaining Misinformation Detection Using Large Language Models. Electronics, 13.","DOI":"10.20944\/preprints202404.1513.v1"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.gltp.2021.08.038","article-title":"Debunking Health Fake News with Domain-Specific Pre-Trained Model","volume":"2","author":"Kumari","year":"2021","journal-title":"Glob. Transit. Proc."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"102569","DOI":"10.1016\/j.ipm.2021.102569","article-title":"Combat COVID-19 Infodemic Using Explainable Natural Language Processing Models","volume":"58","author":"Ayoub","year":"2021","journal-title":"Inf. Process. Manag."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"110642","DOI":"10.1016\/j.knosys.2023.110642","article-title":"Towards COVID-19 Fake News Detection Using Transformer-Based Models","volume":"274","author":"Alghamdi","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Upadhyay, R., Pasi, G., and Viviani, M. (2023). Leveraging Socio-Contextual Information in BERT for Fake Health News Detection in Social Media. Proceedings of the 3rd International Workshop on Open Challenges in Online Social Networks (OASIS \u201923), Rome, Italy, 4\u20138 September 2023, Association for Computing Machinery.","DOI":"10.1145\/3599696.3612902"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"11","DOI":"10.5815\/ijmecs.2024.01.02","article-title":"Explainable Fake News Detection Based on BERT and SHAP Applied to COVID-19","volume":"16","author":"Men","year":"2024","journal-title":"Int. J. Mod. Educ. Comput. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"102737","DOI":"10.1016\/j.mex.2024.102737","article-title":"Detecting Health Misinformation: A Comparative Analysis of Machine Learning and Graph Convolutional Networks in Classification Tasks","volume":"12","author":"Khemani","year":"2024","journal-title":"MethodsX"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Padalko, H., Chomko, V., Yakovlev, S., and Chumachenko, D. (2025). A Novel Comprehensive Framework for Detecting and Understanding Health-Related Misinformation. Information, 16.","DOI":"10.3390\/info16030175"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"20552076251337177","DOI":"10.1177\/20552076251337177","article-title":"Enhancing Medical AI with Retrieval-Augmented Generation: A Mini Narrative Review","volume":"11","author":"Gargari","year":"2025","journal-title":"Digit. Health"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., and Zettlemoyer, L. (2020). BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, 5\u201310 July 2020, Association for Computational Linguistics.","DOI":"10.18653\/v1\/2020.acl-main.703"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/17\/5\/481\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T09:10:25Z","timestamp":1778836225000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/17\/5\/481"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,14]]},"references-count":45,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["info17050481"],"URL":"https:\/\/doi.org\/10.3390\/info17050481","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,14]]}}}