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

Muhammad Abdul-Mageed, AbdelRahim Elmadany, El Moatez Billah Nagoudi, Dinesh Pabbi, Kunal Verma, Rannie Lin

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 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.
Anthology ID:
2021.eacl-main.298
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Editors:
Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3402–3420
Language:
URL:
https://aclanthology.org/2021.eacl-main.298
DOI:
10.18653/v1/2021.eacl-main.298
Bibkey:
Cite (ACL):
Muhammad Abdul-Mageed, AbdelRahim Elmadany, El Moatez Billah Nagoudi, Dinesh Pabbi, Kunal Verma, and Rannie Lin. 2021. Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 3402–3420, Online. Association for Computational Linguistics.
Cite (Informal):
Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19 (Abdul-Mageed et al., EACL 2021)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp22-frontmatter/2021.eacl-main.298.pdf
Code
 UBC-NLP/megacov
Data
Mega-COV
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