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The widespread usage of social networks during mass convergence events, suchas health emergencies and disease outbreaks, provides instant access tocitizen-generated data that carry rich information about public opinions,sentiments, urgent needs, and situational reports. Such information can helpauthorities understand the emergent situation and react accordingly. Moreover,social media plays a vital role in tackling misinformation and disinformation.This work presents TBCOV, a large-scale Twitter dataset comprising more thantwo billion multilingual tweets related to the COVID-19 pandemic collectedworldwide over a continuous period of more than one year. More importantly,several state-of-the-art deep learning models are used to enrich the data withimportant attributes, including sentiment labels, named-entities (e.g.,mentions of persons, organizations, locations), user types, and genderinformation. Last but not least, a geotagging method is proposed to assigncountry, state, county, and city information to tweets, enabling a myriad ofdata analysis tasks to understand real-world issues. Our sentiment and trendanalyses reveal interesting insights and confirm TBCOV's broad coverage ofimportant topics.

TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels