@inproceedings{abdul-mageed-etal-2021-mega,
title = "Mega-{COV}: A Billion-Scale Dataset of 100+ Languages for {COVID}-19",
author = "Abdul-Mageed, Muhammad and
Elmadany, AbdelRahim and
Nagoudi, El Moatez Billah and
Pabbi, Dinesh and
Verma, Kunal and
Lin, Rannie",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.298",
doi = "10.18653/v1/2021.eacl-main.298",
pages = "3402--3420",
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 ({\textasciitilde}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.",
}
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%0 Conference Proceedings
%T Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19
%A Abdul-Mageed, Muhammad
%A Elmadany, AbdelRahim
%A Nagoudi, El Moatez Billah
%A Pabbi, Dinesh
%A Verma, Kunal
%A Lin, Rannie
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F abdul-mageed-etal-2021-mega
%X 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.
%R 10.18653/v1/2021.eacl-main.298
%U https://aclanthology.org/2021.eacl-main.298
%U https://doi.org/10.18653/v1/2021.eacl-main.298
%P 3402-3420
Markdown (Informal)
[Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19](https://aclanthology.org/2021.eacl-main.298) (Abdul-Mageed et al., EACL 2021)
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.