Check-COVID: Fact-Checking COVID-19 News Claims with Scientific Evidence
Gengyu Wang, Kate Harwood, Lawrence Chillrud, Amith Ananthram, Melanie Subbiah, Kathleen R. McKeown · 2023
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 expertannotated 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 indomain data for good performance.Our data and models are released publicly at https: //github.com/posuer/Check-COVID.