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

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, pages 3402–3420 April 19 - 23, 2021. ©2021 Association for Computational Linguistics 3402 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 Natural Language Processing Lab University of British Columbia {muhammad.mageed,a.elmadany,moatez.nagoudi}@ubc.ca, {dinesh09,vkunal96}@ece.ubc.ca, krieyalam@gmail.com 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), multilin- gual (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 pow- erful models, one for identifying whether or not a tweet is related to the pandemic (best F1=97%) and another for detecting misinfor- mation 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 pan- demic. Mega-COV and our models are pub- licly available. 1 Introduction The seeds of the coronavirus disease 2019 (COVID- 19) pandemic are reported to have started as a local outbreak in Wuhan (Hubei, China) in December, 2019, but soon spread around the world (WHO, 2020). As of January 24, 2021, the number of con- firmed cases around the world exceeded 99.14M and the number of confirmed deaths exceeded 2.13M.1 In response to this ongoing public health emergency, researchers are mobilizing to track the pandemic and study its impact on all types of life in the planet. Clearly, the different ways the pandemic has its footprint on human life is a question that will be studied for years to come. Enabling schol- arship on the topic by providing relevant data is an important endeavor. Toward this goal, we collect and release Mega-Cov, a billion-scale multilin- gual Twitter dataset with geo-location information. 1Source: The Center for Systems Science and Engi- neering, Johns Hopkins University. Dashboard: https: //www.arcgis.com/apps/opsdashboard/index. html#/bda7594740fd40299423467b48e9ecf6. Figure 1: Global coverage of Mega-COV based on our geo-located data. Each dot is a city. Contiguous cities of the same color belong to the same country. As a result of the pandemic, most countries around the world went into lockdown and the pub- lic health emergency has restricted physical aspects of human communication considerably. As hun- dreds of millions of people spend more time shel- tering in place, communication over social media became more important than ever. In particular, the content of social media communication promises to capture significant details about the lives of tens of millions of people. Mega-Cov is intended as a repository of such a content. There are several ongoing efforts to collect Twit- ter data, and our goal is to complement these. More specifically, we designed our methods to harvest a dataset that is unique in multiple ways, as follows: Massive Scale: Very large datasets lend them- selves to analyses that are not …

Content truncated to 500 words