Name: Mega-COV
Description:  A Billion-Scale Dataset of 100+ Languages for COVID-19
Provider: University of British Columbia
Paper Title: Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19
Tasks: COVID relevance, COVID misinformation detection
Venue Name: European Chapter of the Association for Computational Linguistics
Authors: Muhammad Abdul-Mageed,AbdelRahim Elmadany,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.