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Over the course of the COVID-19 pandemic, large volumes of biomedicalinformation concerning this new disease have been published on social media.Some of this information can pose a real danger to people's health,particularly when false information is shared, for instance recommendations onhow to treat diseases without professional medical advice. Therefore, automaticfact-checking resources and systems developed specifically for the medicaldomain are crucial. While existing fact-checking resources coverCOVID-19-related information in news or quantify the amount of misinformationin tweets, there is no dataset providing fact-checked COVID-19-related Twitterposts with detailed annotations for biomedical entities, relations and relevantevidence. We contribute CoVERT, a fact-checked corpus of tweets with a focus onthe domain of biomedicine and COVID-19-related (mis)information. The corpusconsists of 300 tweets, each annotated with medical named entities andrelations. We employ a novel crowdsourcing methodology to annotate all tweetswith fact-checking labels and supporting evidence, which crowdworkers searchfor online. This methodology results in moderate inter-annotator agreement.Furthermore, we use the retrieved evidence extracts as part of a fact-checkingpipeline, finding that the real-world evidence is more useful than theknowledge indirectly available in pretrained language models.

CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets