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Misinformation is an ever increasing problem that is difficult to solve forthe research community and has a negative impact on the society at large. Veryrecently, the problem has been addressed with a crowdsourcing-based approach toscale up labeling efforts: to assess the truthfulness of a statement, insteadof relying on a few experts, a crowd of (non-expert) judges is exploited. Wefollow the same approach to study whether crowdsourcing is an effective andreliable method to assess statements truthfulness during a pandemic. Wespecifically target statements related to the COVID-19 health emergency, thatis still ongoing at the time of the study and has arguably caused an increaseof the amount of misinformation that is spreading online (a phenomenon forwhich the term "infodemic" has been used). By doing so, we are able to address(mis)information that is both related to a sensitive and personal issue likehealth and very recent as compared to when the judgment is done: two issuesthat have not been analyzed in related work. In our experiment, crowd workersare asked to assess the truthfulness of statements, as well as to provideevidence for the assessments as a URL and a text justification. Besides showingthat the crowd is able to accurately judge the truthfulness of the statements,we also report results on many different aspects, including: agreement amongworkers, the effect of different aggregation functions, of scalestransformations, and of workers background / bias. We also analyze workersbehavior, in terms of queries submitted, URLs found / selected, textjustifications, and other behavioral data like clicks and mouse actionscollected by means of an ad hoc logger.

The COVID-19 Infodemic: Can the Crowd Judge Recent Misinformation Objectively?