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