Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 May 2020.
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Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 1.
5 papers.
- 2021Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detectioncited by 14
- 2022ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccinationcited by 3
- 2022NusaCrowd: A Call for Open and Reproducible NLP Research in Indonesian Languagescited by 2
- 2023A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformationcited by 0
- 2024Health Misinformation in Social Networks: A Survey of IT Approachescited by 0
- 2024Health Misinformation in Social Networks: A Survey of IT Approaches24 Oct 2024
- 2023A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformation10 Apr 2023
- 2022NusaCrowd: A Call for Open and Reproducible NLP Research in Indonesian Languages21 Jul 2022
- 2022ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination17 Jan 2022
- 2021Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detection23 Sep 2021