HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking

TL;DR

This paper introduces HealthFC, a new multilingual dataset of 750 health claims with expert-verified evidence, aimed at advancing automated fact-checking in medical information through NLP.

Contribution

The paper presents a novel, expert-labeled dataset for health claim verification, along with baseline models and analysis of its challenges and potential for NLP research.

Findings

The dataset is challenging for current NLP models.

Baseline systems show room for improvement.

HealthFC enables research in evidence retrieval and claim verification.

Abstract

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.