Check-COVID: Fact-Checking COVID-19 News Claims with Scientific Evidence

• URL: http://arxiv.org/abs/2305.18265v1
• Date: Mon, 29 May 2023 17:39:22 GMT
• Title: Check-COVID: Fact-Checking COVID-19 News Claims with Scientific Evidence
• Authors: Gengyu Wang, Kate Harwood, Lawrence Chillrud, Amith Ananthram, Melanie Subbiah, Kathleen McKeown
• Abstract summary: Check-COVID contains 1, 504 expert-annotated news claims about the coronavirus paired with sentence-level evidence from scientific journal articles and veracity labels. It includes both extracted (journalist-written) and composed (annotator-written) claims.
• Score: 6.443863980834019
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a new fact-checking benchmark, Check-COVID, that requires systems to verify claims about COVID-19 from news using evidence from scientific articles. This approach to fact-checking is particularly challenging as it requires checking internet text written in everyday language against evidence from journal articles written in formal academic language. Check-COVID contains 1, 504 expert-annotated news claims about the coronavirus paired with sentence-level evidence from scientific journal articles and veracity labels. It includes both extracted (journalist-written) and composed (annotator-written) claims. Experiments using both a fact-checking specific system and GPT-3.5, which respectively achieve F1 scores of 76.99 and 69.90 on this task, reveal the difficulty of automatically fact-checking both claim types and the importance of in-domain data for good performance. Our data and models are released publicly at https://github.com/posuer/Check-COVID.

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