Ginger Cannot Cure Cancer: Battling Fake Health News with a Comprehensive Data Repository
This work addresses the challenge of detecting fake health news, which threatens public health, by providing a domain-specific dataset, though it is incremental as it builds on existing fake news detection research.
The authors tackled the problem of fake health news detection by constructing a comprehensive data repository called FakeHealth, which includes news content with rich features, reviews with explanations, social engagements, and user networks, and conducted exploratory analyses to validate its quality for research.
Nowadays, Internet is a primary source of attaining health information. Massive fake health news which is spreading over the Internet, has become a severe threat to public health. Numerous studies and research works have been done in fake news detection domain, however, few of them are designed to cope with the challenges in health news. For instance, the development of explainable is required for fake health news detection. To mitigate these problems, we construct a comprehensive repository, FakeHealth, which includes news contents with rich features, news reviews with detailed explanations, social engagements and a user-user social network. Moreover, exploratory analyses are conducted to understand the characteristics of the datasets, analyze useful patterns and validate the quality of the datasets for health fake news detection. We also discuss the novel and potential future research directions for the health fake news detection.