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Detecting False Information in Medical and Healthcare Domains: A Text Mining Approach

  • Jiexun LiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11924)

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.

Keywords

False information Medical Healthcare Text mining 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Western Washington UniversityBellinghamUSA

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