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The COVID-19 pandemic led to an infodemic where an overwhelming amount ofCOVID-19 related content was being disseminated at high velocity through socialmedia. This made it challenging for citizens to differentiate between accurateand inaccurate information about COVID-19. This motivated us to carry out acomparative study of the characteristics of COVID-19 misinformation versusthose of accurate COVID-19 information through a large-scale computationalanalysis of over 242 million tweets. The study makes comparisons alongside fourkey aspects: 1) the distribution of topics, 2) the live status of tweets, 3)language analysis and 4) the spreading power over time. An added contributionof this study is the creation of a COVID-19 misinformation classificationdataset. Finally, we demonstrate that this new dataset helps improvemisinformation classification by more than 9\% based on average F1 measure.

A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformation