HealthFC – Verifying Health Claims with Evidence-Based Medical Fact-Checking
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.
| Attribute | Value |
|---|---|
| Address | Torino, Italy |
| Authors | Juraj Vladika , Phillip Schneider , Prof. Dr. Florian Matthes |
| Citation | Juraj Vladika, Phillip Schneider, and Florian Matthes. 2024. HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 8095–8107, Torino, Italia. ELRA and ICCL. |
| Key | |
| Research project | Scientific Claim Verification with Evidence from Text and Structured Knowledge (VeriSci) |
| Title | HealthFC: Verifying Health Claims with Evidence-Based Medical Fact-Checking |
| Type of publication | Conference |
| Year | 2024 |
| Publication URL | https://aclanthology.org/2024.lrec-main.709/ |
| Acronym | LREC-COLING 2024 |
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