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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.18670 |
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| _version_ | 1866910664748957696 |
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| author | Papanikou, Vasiliki Papadakos, Panagiotis Karamanidou, Theodora Stavropoulos, Thanos G. Pitoura, Evaggelia Tsaparas, Panayiotis |
| author_facet | Papanikou, Vasiliki Papadakos, Panagiotis Karamanidou, Theodora Stavropoulos, Thanos G. Pitoura, Evaggelia Tsaparas, Panayiotis |
| contents | In this paper, we present a comprehensive survey on the pervasive issue of medical misinformation in social networks from the perspective of information technology. The survey aims at providing a systematic review of related research and helping researchers and practitioners navigate through this fast-changing field. Specifically, we first present manual and automatic approaches for fact-checking. We then explore fake news detection methods, using content, propagation features, or source features, as well as mitigation approaches for countering the spread of misinformation. We also provide a detailed list of several datasets on health misinformation and of publicly available tools. We conclude the survey with a discussion on the open challenges and future research directions in the battle against health misinformation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_18670 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Health Misinformation in Social Networks: A Survey of IT Approaches Papanikou, Vasiliki Papadakos, Panagiotis Karamanidou, Theodora Stavropoulos, Thanos G. Pitoura, Evaggelia Tsaparas, Panayiotis Social and Information Networks Artificial Intelligence Computation and Language Machine Learning In this paper, we present a comprehensive survey on the pervasive issue of medical misinformation in social networks from the perspective of information technology. The survey aims at providing a systematic review of related research and helping researchers and practitioners navigate through this fast-changing field. Specifically, we first present manual and automatic approaches for fact-checking. We then explore fake news detection methods, using content, propagation features, or source features, as well as mitigation approaches for countering the spread of misinformation. We also provide a detailed list of several datasets on health misinformation and of publicly available tools. We conclude the survey with a discussion on the open challenges and future research directions in the battle against health misinformation. |
| title | Health Misinformation in Social Networks: A Survey of IT Approaches |
| topic | Social and Information Networks Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2410.18670 |