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• W3024691813 abstract "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." @default.
• W3024691813 created "2020-05-21" @default.
• W3024691813 creator A5004288665 @default.
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• W3024691813 date "2020-05-02" @default.
• W3024691813 modified "2023-09-27" @default.
• W3024691813 title "Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19" @default.
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• W3024691813 doi "https://doi.org/10.48550/arxiv.2005.06012" @default.
• W3024691813 hasPublicationYear "2020" @default.
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