Abstract
In recent years, a lot of false information in medical and healthcare domains has emerged and spread over the Internet. Such false information has become a big risk to public health and safety. This study investigates this problem by analyzing data collected from two fact-checking websites, 416 medical claims from Snopes.com and 1,692 healthcare-related statements from PolitiFact.com. Topic analysis reveals frequent words and common topics occurring in these claims spread online. Furthermore, using text-mining and machine-learning techniques, this study builds prediction models for detecting false information and shows promising performance. Several textual and source features are identified as good indicators for true or false information in medical and healthcare domains.
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Li, J. (2019). Detecting False Information in Medical and Healthcare Domains: A Text Mining Approach. In: Chen, H., Zeng, D., Yan, X., Xing, C. (eds) Smart Health. ICSH 2019. Lecture Notes in Computer Science(), vol 11924. Springer, Cham. https://doi.org/10.1007/978-3-030-34482-5_21
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DOI: https://doi.org/10.1007/978-3-030-34482-5_21
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