HealthFC: A Dataset of Health Claims for Evidence-Based Medical Fact-Checking
Seeking health-related advice on the internet has become a common practice in
the digital era. Determining the trustworthiness of medical claims found online
and finding appropriate evidence for this information 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
the automation of this task, in this paper, we introduce a novel dataset of 750
health-related claims, labeled for veracity by medical experts and backed with
evidence from appropriate clinical studies. We provide an analysis of the
dataset, highlighting its characteristics and challenges. The dataset can be
used for Machine Learning tasks related to automated fact-checking such as
evidence retrieval, veracity prediction, and explanation generation. For this
purpose, we provide baseline models based on different approaches, examine
their performance, and discuss the findings.