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https://dagshub.com/syedzubeen/health_fact
PUBHEALTH is a comprehensive dataset for explainable automated fact-checking of public health claims. Each instance in the PUBHEALTH dataset has an associated veracity label (true, false, unproven, mixture). Furthermore, each instance in the dataset has an explanation text field. The explanation is a justification for which the claim has been assigned a particular veracity label.
@inproceedings{kotonya-toni-2020-explainable, title = "Explainable Automated Fact-Checking for Public Health Claims", author = "Kotonya, Neema and Toni, Francesca", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-main.623", pages = "7740--7754", }
The PUBHEALTH Dataset is released under the MIT License.
The dataset was created to explore fact-checking of difficult-to-verify claims i.e., those that require expertise from outside of the journalistics domain, in this case, biomedical and public health expertise.
It was also created in response to the lack of fact-checking datasets which provide gold-standard natural language explanations for verdicts/labels. The dataset was created by Neema Kotonya, and Francesca Toni, for their research paper "Explainable Automated Fact-Checking for Public Health Claims" presented at EMNLP 2020.
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