Evidence-based approach to verification of online health-related content

While advances in machine learning (ML) and natural language processing (NLP) offer potential tools to help identify false health information, existing research has largely focused on political news. Health misinformation requires distinct approaches due to its reliance on current, reliable medical resources, which aren’t easily accessible in traditional fact-checking systems.

This thesis addresses the verification of online health information using evidence-based medicine (EBM), emphasizing scientific rigor, reliable sources, and up-to-date knowledge. Given the scarcity of labeled data for training, the study explores unsupervised methods and transfer learning techniques, comparing these to existing supervised approaches. The thesis proposes a novel unsupervised method for detecting health claims and retrieving relevant scholarly resources.