文章基本信息

标题：Mega-COV: A Billion-Scale Dataset of 100+ Languages forCOVID-19
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作者：Muhammad Abdul-Mageed ; AbdelRahim Elmadany ; El Moatez Billah Nagoudi 等
期刊名称：Conference on European Chapter of the Association for Computational Linguistics (EACL)
出版年度：2021
卷号：2021
页码：3402-3420
DOI：10.18653/v1/2021.eacl-main.298
语种：English
出版社：ACL Anthology
摘要：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.