HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking
This addresses the challenge of assessing trustworthiness in online medical content for users and researchers, though it is incremental as it builds on existing fact-checking datasets by focusing on health claims.
The paper tackles the problem of verifying health claims by introducing HealthFC, a dataset of 750 health-related claims in German and English labeled by medical experts with evidence from systematic reviews and clinical trials, and provides baseline systems for automated fact-checking tasks.
In the digital age, seeking health advice on the Internet has become a common practice. At the same time, determining the trustworthiness of online medical content is increasingly challenging. Fact-checking has emerged as an approach to assess the veracity of factual claims using evidence from credible knowledge sources. To help advance automated Natural Language Processing (NLP) solutions for this task, in this paper we introduce a novel dataset HealthFC. It consists of 750 health-related claims in German and English, labeled for veracity by medical experts and backed with evidence from systematic reviews and clinical trials. We provide an analysis of the dataset, highlighting its characteristics and challenges. The dataset can be used for NLP tasks related to automated fact-checking, such as evidence retrieval, claim verification, or explanation generation. For testing purposes, we provide baseline systems based on different approaches, examine their performance, and discuss the findings. We show that the dataset is a challenging test bed with a high potential for future use.