Check-COVID: Fact-Checking COVID-19 News Claims with Scientific Evidence
Citations Over TimeTop 15% of 2023 papers
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.
Related Papers
- → And now for something completely different: from 2019-nCoV and COVID-19 to 2020-nMan(2020)27 cited
- → ОПЫТ ОЦЕНКИ ПОПУЛЯЦИОНОГО ИММУНИТЕТА К SARS-CoV-2 СРЕДИ НАСЕЛЕНИЯ ЛЕНИНГРАДСКОЙ ОБЛАСТИ В ПЕРИОД ЭПИДЕМИИ COVID-19(2020)7 cited
- → Confirmation of SARS-CoV-2 airborne dissemination indoors using “COVID-19 traps”(2021)4 cited
- → ОПЫТ ИССЛЕДОВАНИЯ СЕРОПРЕВАЛЕНТНОСТИ К ВИРУСУ SARS-CoV-2 НАСЕЛЕНИЯ ИРКУТСКОЙ ОБЛАСТИ В ПЕРИОД ВСПЫШКИ COVID-19(2020)2 cited