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While misinformation and disinformation have been thriving in social mediafor years, with the emergence of the COVID-19 pandemic, the political and thehealth misinformation merged, thus elevating the problem to a whole new leveland giving rise to the first global infodemic. The fight against this infodemichas many aspects, with fact-checking and debunking false and misleading claimsbeing among the most important ones. Unfortunately, manual fact-checking istime-consuming and automatic fact-checking is resource-intense, which meansthat we need to pre-filter the input social media posts and to throw out thosethat do not appear to be check-worthy. With this in mind, here we propose amodel for detecting check-worthy tweets about COVID-19, which combines deepcontextualized text representations with modeling the social context of thetweet. We further describe a number of additional experiments and comparisons,which we believe should be useful for future research as they provide someindication about what techniques are effective for the task. Our officialsubmission to the English version of CLEF-2020 CheckThat! Task 1, systemTeam_Alex, was ranked second with a MAP score of 0.8034, which is almost tiedwith the wining system, lagging behind by just 0.003 MAP points absolute.

Team Alex at CLEF CheckThat! 2020: Identifying Check-Worthy Tweets With Transformer Models