{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/covert-a-corpus-of-fact-checked-biomedical","title":"CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweets","arxiv_id":"2204.12164","date":"2022-04-26","proceeding":"LREC 2022 6","authors":["Isabelle Mohr","Amelie Wührl","Roman Klinger"],"abstract":"Over the course of the COVID-19 pandemic, large volumes of biomedical information 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 on how to treat diseases without professional medical advice. Therefore, automatic fact-checking resources and systems developed specifically for the medical domain are crucial. While existing fact-checking resources cover COVID-19-related information in news or quantify the amount of misinformation in tweets, there is no dataset providing fact-checked COVID-19-related Twitter posts with detailed annotations for biomedical entities, relations and relevant evidence. We contribute CoVERT, a fact-checked corpus of tweets with a focus on the domain of biomedicine and COVID-19-related (mis)information. The corpus consists of 300 tweets, each annotated with medical named entities and relations. We employ a novel crowdsourcing methodology to annotate all tweets with fact-checking labels and supporting evidence, which crowdworkers search for online. This methodology results in moderate inter-annotator agreement. Furthermore, we use the retrieved evidence extracts as part of a fact-checking pipeline, finding that the real-world evidence is more useful than the knowledge indirectly available in pretrained language models.","url_abs":"https://arxiv.org/abs/2204.12164v1","url_pdf":"https://arxiv.org/pdf/2204.12164v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"fact-checking","task_name":"Fact Checking"},{"task_slug":"misinformation","task_name":"Misinformation"}],"methods":[],"datasets_introduced":[{"slug":"covert","name":"CoVERT","full_name":"A Corpus of Fact-checked Biomedical COVID-19 Tweets"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2204.12164","atlas_url":"https://app.syntology.ai/?focus=2204.12164","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}