Mega- COV : A Billion-Scale Dataset of 100+ Languages for COVID -19 (2021)

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.

## 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 confirmed 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 scholarship on the topic by providing relevant data is an important endeavor. toward this goal, we collect and release mega-cov, a billion-scale multilingual twitter dataset with geo-location information. as a result of the pandemic, most countries around the world went into lockdown and the public health emergency has restricted physical aspects of human communication considerably. as hundreds of millions of people spend more time sheltering 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 twitter 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 themselves to analyses that are not possible with smaller data. given the global nature of covid-19, we realize that a large-scale dataset will be most useful as the scale allows for slicing and dicing the data across different times, communities, languages, and regions that are not possible otherwise. for this reason, we dedicated significant resources to harvesting and preparing the dataset. mega-cov has solid international coverage and brings data from 1m users from 268 countries (see section 3.1). overall, our dataset has ∼ 1.5b tweets (section 2). this is one order of magnitude larger than #covid-19 @xcite , the largest dataset we know of (∼ 144m tweets as of june 1, 2020). 2 topic diversity: we do not restrict our collection to tweets carrying certain hashtags. this makes the data general enough to involve content and topics directly related to covid-19, regardless of existence of accompanying hashtags. this also allows for investigating themes that may not be directly linked to the pandemic but where the pandemic may have some bearings which should be taken into account when investigating such themes. this is important because users can, and indeed do, post about activities impacted by the health crisis without using any hashtags. in fact, users may not mention covid-19 at all, even though what they are posting about could be affected by the pandemic one way or another (e.g., "eating habits", "shopping behavior"). section b and section c in the appendix provide a general overview of issues discussed in the dataset. ## data collection to collect a sufficiently large dataset, we put crawlers using the twitter streaming api foot_1 on africa, asia, australia, europe, north america, and south america starting in early january, 2020. this allows us to acquire a diverse set of tweets from which we can extract a random set of user ids whose timelines (up to 3,200 tweets) we then iteratively crawl every two weeks. this gave us data from july 30 th , 2020 backwards, depending on how prolific of a poster a user is (see mega-cov comprises a total of 1, 023, 972 users who contribute 1, 487, 328, 805 tweets. for each tweet, we collect the whole json object. this gives us access to various types of information, such as user location and the language tag (including "undefined") twitter assigns to each tweet. we then use the data streaming and processing engine, spark, to merge all user files and run our analyses. to capture a wide range of behaviors, we keep tweets, retweets, and responses (i.e., direct user-touser interactions) as independent categories. ## models we develop two groups of models suited for answering important questions related to covid-19, including making use of mega-cov. these are (1) covid-relevance, where a classifier will lang freq lang freq english (en) 900m hebrew (he) 1.2m spanish (es) 122m croatian (hr) 685k portuguese (pt) 79.5m maltese (mt) 325k japanese (ja) 46.6m slovak (sk) 246k arabic (ar) 45m bi (id) 208k indonesion (in) 37m latin (la) 183k french (fr) 29.5m bosnian (bs) 143k turkish (tr) 28.5m dzongkha (dz) 137.8k tagalog (tl) 19m swahili (sw) 92k italian (it) 8.8m azerbaijani (az) 68.9k thai (th) 7.7m quechua (qu) 61k hindi (hi) 7m albanian (sq) 61k dutch (nl) 6.9m malay (ms) 59k russuian (ru) 6.2m kinyarwanda (rw) 56.8k german (de) 6m esperanto (eo) 55k catalan (ca) 3.5m javanese (jv) 53k korean (ko) 2.9m xhosa (xh) 47.7k haitian creole (ht) 2.8m irish (ga) 44.6k polish (pl) 2.4m kurdish (ku) 43k estonain (et) 2.1m volapük (vo) 41k ## applications on mega-cov now that we have developed two highly effective models, one for covid-relevance and another for covid-misinformation, we can employ these models to make discoveries using mega-cov. since 10 even though we could have used the monolingual versions of the transformer-based language models (i.e., bert and roberta), we stick to the multilingual versions for consistency. we then apply the xlm-r large relevance and misinformation models on the extracted samples. ## conclusion we presented mega-cov, a billion-scale dataset of 104 languages for studying covid-19 pandemic. in addition to being large and highly multilingual, our dataset comprises data pre-dating the pandemic. this allows for comparative and longitudinal investigations. we provided a global description of mega-cov in terms of its geographic and temporal coverage, over-viewed its linguistic diversity, and provided analysis of its content based on hashtags and top domains. we also provided a case study of how the data can be used to track global human mobility. the scale of the mega-cov has also allowed us to make a number of striking discoveries, including (1) the shift toward retweeting and replying to other users rather than tweeting in 2020 and (2) the role of international news sites as key sources of information during the pandemic. in addition, we developed effective models for detecting covid relevance and covid misinformation and applied them to a large sample of our dataset. our dataset and models are publicly available.