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

Abstract

The widespread usage of social networks during mass convergence events, such as health emergencies and disease outbreaks, provides instant access to citizen-generated data that carry rich information about public opinions, sentiments, urgent needs, and situational reports. Such information can help authorities 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 than two billion multilingual tweets related to the COVID-19 pandemic collected worldwide 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 with important attributes, including sentiment labels, named-entities (e.g., mentions of persons, organizations, locations), user types, and gender information. Last but not least, a geotagging method is proposed to assign country, state, county, and city information to tweets, enabling a myriad of data analysis tasks to understand real-world issues. Our sentiment and trend analyses reveal interesting insights and confirm TBCOV's broad coverage of important topics.

20 pages, 13 figures, 8 tables

References in corpus (7)

• A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration
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Cited by in corpus (6)

• Where did you tweet from? Inferring the origin locations of tweets based on contextual information
• A Real-time System for Detecting Landslide Reports on Social Media using Artificial Intelligence
• BillionCOV: An Enriched Billion-scale Collection of COVID-19 tweets for Efficient Hydration
• Leave no Place Behind: Improved Geolocation in Humanitarian Documents
• A Twitter narrative of the COVID-19 pandemic in Australia
• Cutting through the noise to motivate people: A comprehensive analysis of COVID-19 social media posts de/motivating vaccination