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.

VaccinEU: COVID-19 Vaccine Conversations on Twitter in French, German and Italian

“…In addition to the aforementioned resources, several datasets have been released to study the COVID-19 pandemic on Twitter, providing oftentimes useful metadata (geolocation, sentiment, gender, etc) in addition to raw tweet ids (Banda et al 2021;Chen et al 2020;Lopez and Gallemore 2021;Imran, Qazi, and Ofli 2021).…”

A large-scale analysis of COVID-19 tweets in the Arab region

“…To study the discourse around COVID-19 in the Arab region, we use the TBCOV dataset (Imran et al 2021), an extension of the GeoCoV-19 dataset we sampled and annotated to train our models. TBCOV consists of two billion multilingual and geolocated tweets about COVID-19 spanning from February 1, 2020, until March 31, 2021.…”

“…The three labeled datasets described above were then used to train multiple deep learning models, including CNN, BiL-STM, and BERT to automatically classify a tweet related to COVID-19 into one of the 11 topics we have identified. The best classifier BERT was then applied on a second geotagged dataset of tweets that also contains tweets related to COVID-19 spanning the period from February 1, 2020, to March 31, 2021 (Imran et al 2021).…”