Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues
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Abstract (arXiv)
Social media has been one of the main information consumption sources for the public, allowing people to seek and spread information more quickly and easily. However, the rise of various social media platforms also enables the proliferation of online misinformation. In particular, misinformation in the health domain has significant impacts on our society such as the COVID-19 infodemic. Therefore, health misinformation in social media has become an emerging research direction that attracts increasing attention from researchers of different disciplines. Compared to misinformation in other domains, the key differences of health misinformation include the potential of causing actual harm to humans' bodies and even lives, the hardness to identify for normal people, and the deep connection with medical science. In addition, health misinformation on social media has distinct characteristics from conventional channels such as television on multiple dimensions including the generation, dissemination, and consumption paradigms. Because of the uniqueness and importance of combating health misinformation in social media, we conduct this survey to further facilitate interdisciplinary research on this problem. In this survey, we present a comprehensive review of existing research about online health misinformation in different disciplines. Furthermore, we also systematically organize the related literature from three perspectives: characterization, detection, and intervention. Lastly, we conduct a deep discussion on the pressing open issues of combating health misinformation in social media and provide future directions for multidisciplinary researchers.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 64 of its 242 references that are in the map. First posted on arXiv 10 Nov 2022.
Builds on
64 of 242 references.
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Citing papers per year: 2023: 3; 2024: 3.
6 papers.
- 2023Can LLM-Generated Misinformation Be Detected?cited by 173
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- 2023MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learningcited by 8
- 2023Identifying Misinformation on YouTube through Transcript Contextual Analysis with Transformer Modelscited by 2
- 2024Health Misinformation in Social Networks: A Survey of IT Approachescited by 0
- 2024Health Misinformation in Social Networks: A Survey of IT Approaches24 Oct 2024
- 2024Can Knowledge Editing Really Correct Hallucinations?21 Oct 2024
- 2024Can Editing LLMs Inject Harm?29 Jul 2024
- 2023Can LLM-Generated Misinformation Be Detected?25 Sep 2023
- 2023Identifying Misinformation on YouTube through Transcript Contextual Analysis with Transformer Models22 Jul 2023
- 2023MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning22 May 2023