Health Misinformation Detection: Approaches, Challenges and Opportunities.
To mitigate the rapid spread of health misinformation and its negative impact, this study presents a comprehensive literature review on health misinformation detection. A systematic search is conducted using the Google Scholar database, targeting publications from January 2016 to February 2025. Incl...
| Publicado en: | Inquiry (00469580) Vol. 62; pp. 1 - 36 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
| Publicado: |
Sage Publications Inc.
11/4/2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=189325283&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 189325283 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00469580 INQ jtl: Inquiry (00469580) issn: 00469580 maglogo: Y pubinfo: dt: 11/4/2025 vid: 62 pid: 344 pub: Sage Publications Inc. artinfo: ui: 189325283 10.1177/00469580251384784 ppf: 1 ppct: 35 formats: tig: atl: Health Misinformation Detection: Approaches, Challenges and Opportunities. aug: au: Feng, Xiaoye Luo, Jia Yang, Yang El Baz, Didier Shi, Lei affil: College of Economics and Management, Beijing University of Technology, Beijing, China Chongqing Research Institute, Beijing University of Technology, Chongqing, China LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China su: Boosting algorithms Social media Health literacy Medical logic Random forest algorithms Language & languages Crowdsourcing Data analysis Interprofessional relations Disinformation Memory bias Health status indicators Health At-risk people Privacy Data curation Health policy Phonological awareness Misinformation Information resources Natural language processing Anxiety Emotions Psychological well-being Affective disorders Blockchains Support vector machines Detection algorithms Intention Psychological stress Trust Artificial neural networks Metadata Psychometrics Mathematical models Machine learning Concepts Semantics Sociodemographic factors Accuracy Resource-limited settings Public health Decision trees Theory Information resources management Algorithms Sensitivity & specificity (Statistics) Medical ethics Social isolation Professional competence Educational attainment Genetics Cognition Generalized anxiety disorder Mental depression Psychosocial factors sug: subj: Boosting algorithms Social media Health literacy Medical logic Random forest algorithms Language & languages Crowdsourcing Data analysis Interprofessional relations Disinformation Memory bias Health status indicators Health At-risk people Privacy Data curation Health policy Phonological awareness Misinformation Information resources Natural language processing Anxiety Emotions Psychological well-being Affective disorders Blockchains Support vector machines Detection algorithms Intention Psychological stress Trust Artificial neural networks Metadata Psychometrics Mathematical models Machine learning Concepts Semantics Sociodemographic factors Accuracy Resource-limited settings Public health Decision trees Theory Information resources management Algorithms Sensitivity & specificity (Statistics) Medical ethics Social isolation Professional competence Educational attainment Genetics Cognition Generalized anxiety disorder Mental depression Psychosocial factors keyword: concepts and analysis datasets and metrics deep learning health misinformation detection machine learning methodologies ab: To mitigate the rapid spread of health misinformation and its negative impact, this study presents a comprehensive literature review on health misinformation detection. A systematic search is conducted using the Google Scholar database, targeting publications from January 2016 to February 2025. Inclusion criteria require full-text, English-language studies proposing health misinformation detection methods. A total of 100 relevant studies are included. The characteristics of health misinformation are identified through a detailed analysis of its concept, dissemination mechanism, psychological impact, and susceptibility. Datasets and evaluation metrics are reviewed, with issues such as class imbalance and inconsistencies in annotation standards being identified. The strengths and limitations of various detection approaches are examined. Machine learning approaches perform better when using ensemble methods, feature selection techniques, and embedding-based representations. Deep learning algorithms are strong in automatic feature extraction and high-dimensional semantic modeling, though they often face challenges such as high computational cost and low interpretability. Advanced detection methods show clear improvements in accuracy and explainability, while also introducing AI-generated misinformation and associated ethical concerns. This review provides a panoramic view of the current state-of-the-art in health misinformation detection. It further underscores the importance of interdisciplinary collaboration, human-centered design, and ethical considerations for the development of effective and clinically relevant detection systems. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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