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Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19

We describe Mega-COV, a billion-scale dataset from Twitter for studying COVID-19. The dataset is diverse (covers 268 countries), longitudinal (goes as back as 2007), multilingual (comes in 100+ languages), and has a significant number of location-tagged tweets (~169M tweets). We release tweet IDs from the dataset. We also develop and release two powerful models, one for identifying whether or not a tweet is related to the pandemic (best F1=97%) and another for detecting misinformation about COVID-19 (best F1=92%). A human annotation study reveals the utility of our models on a subset of Mega-COV. Our data and models can be useful for studying a wide host of phenomena related to the pandemic. Mega-COV and our models are publicly available.

preprint2021arXivOpen access
Muhammad Abdul-MageedAbdelRahim ElmadanyEl Moatez Billah NagoudiDinesh PabbiKunal VermaRannie Lin
Computation and LanguageSocial and Information Networks
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Open access6 authors2 topics
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Muhammad Abdul-MageedAbdelRahim ElmadanyEl Moatez Billah NagoudiDinesh PabbiKunal VermaRannie Lin

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