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Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues

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arxiv 2211.05289 v1 pith:FWTRHMYU submitted 2022-11-10 cs.SI cs.AIcs.CY

classification cs.SIcs.AIcs.CY
keywords misinformationhealthmediasocialcombatingresearchcharacterizationconduct
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Robustness Evaluation of Graph-based News Detection Using Network Structural Information

    cs.SI 2025-05 conditional novelty 5.0 of 10

    SI2AF attacks GNN fake news detectors by using structural entropy to find post communities and coordinating bot, cyborg, and worker agents to connect malicious accounts to related posts.

  2. A Survey on Proactive Defense Strategies Against Misinformation in Large Language Models

    cs.IR 2025-07 reject novelty 3.0 of 10

    A survey claims proactive defenses against LLM misinformation outperform post-hoc detection by up to 63%, but no meta-analysis details are provided to support the claim.

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