Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society

With the emergence of the COVID-19 pandemic, the political and the medical\naspects of disinformation merged as the problem got elevated to a whole new\nlevel to become the first global infodemic. Fighting this infodemic has been\ndeclared one of the most important focus areas of the World Health\nOrganization, with dangers ranging from promoting fake cures, rumors, and\nconspiracy theories to spreading xenophobia and panic. Addressing the issue\nrequires solving a number of challenging problems such as identifying messages\ncontaining claims, determining their check-worthiness and factuality, and their\npotential to do harm as well as the nature of that harm, to mention just a few.\nTo address this gap, we release a large dataset of 16K manually annotated\ntweets for fine-grained disinformation analysis that (i) focuses on COVID-19,\n(ii) combines the perspectives and the interests of journalists, fact-checkers,\nsocial media platforms, policy makers, and society, and (iii) covers Arabic,\nBulgarian, Dutch, and English. Finally, we show strong evaluation results using\npretrained Transformers, thus confirming the practical utility of the dataset\nin monolingual vs. multilingual, and single task vs. multitask settings.\n