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Computer Science > Computation and Language

arXiv:2305.18265 (cs)
COVID-19 e-print

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[Submitted on 29 May 2023]

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
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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 this https URL.
Comments: Accepted as ACL 2023 Findings
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
Cite as: arXiv:2305.18265 [cs.CL]
  (or arXiv:2305.18265v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2305.18265

Submission history

From: Gengyu Wang [view email]
[v1] Mon, 29 May 2023 17:39:22 UTC (7,866 KB)
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