COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic

We introduce a FEVER-like dataset COVID-Fact of 4, 086 claims concerning the COVID-19 pandemic. The dataset contains claims, evidence for the claims, and contradictory claims refuted by the evidence. Unlike previous approaches, we automatically detect true claims and their source articles and then generate counter-claims using automatic methods rather than employing human annotators. Along with our constructed resource, we formally present the task of identifying relevant evidence for the claims and verifying whether the evidence refutes or supports a given claim. In addition to scientific claims, our data contains simplified general claims from media sources, making it better suited for detecting general misinformation regarding COVID-19. Our experiments indicate that COVID-Fact will provide a challenging testbed for the development of new systems and our approach will reduce the costs of building domainspecific datasets for detecting misinformation. Original Claim Closed environments facilitate secondary transmission of coronavirus disease 2019 Counter-Claim Closed environments prevent secondary transmission of coronavirus disease 2019 Gold Document https://www.medrxiv.org/content/10.1101/2020.02.28.20029272v2 Gold Evidence It is plausible that closed environments contribute to secondary transmission of COVID-19 and promote superspreading events. Original Claim Oxford vaccine triggers immune response Counter-Claim Oxford vaccine inhibits immune response Gold Document https://www.bbc.com/news/uk-53469839 Gold Evidence They are injecting coronavirus RNA (its genetic code), which then starts making viral proteins in order to trigger an immune response.