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.
@article{arxiv.2005.06012,
title = {Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19},
author = {Muhammad Abdul-Mageed and AbdelRahim Elmadany and El Moatez Billah Nagoudi and Dinesh Pabbi and Kunal Verma and Rannie Lin},
journal= {arXiv preprint arXiv:2005.06012},
year = {2021}
}