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

arXiv: https://arxiv.org/abs/2110.03664 · HTML: https://mlsanity.com/paper/2110.03664/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 78 references that are in the map.
- First posted on arXiv 4 Oct 2021.

## Builds on (most cited first, 10 of 10)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [Deep Learning for Sentiment Analysis : A Survey](https://mlsanity.com/paper/1801.07883/) (2018)
- [A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration](https://mlsanity.com/paper/2004.03688/) (2020)
- [Multiscale Dynamic Human Mobility Flow Dataset in the U.S. during the COVID-19 Epidemic](https://mlsanity.com/paper/2008.12238/) (2020)
- [GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information](https://mlsanity.com/paper/2005.11177/) (2020)
- [Large Arabic Twitter Dataset on COVID-19](https://mlsanity.com/paper/2004.04315/) (2020)
- [ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks](https://mlsanity.com/paper/2004.05861/) (2020)
- [A survey of location inference techniques on Twitter](https://mlsanity.com/paper/1701.03639/) (2017)
- [Understanding Types of Users on Twitter](https://mlsanity.com/paper/1406.1335/) (2014)

## Built on by (most cited first, 11 of 11)

- [Where did you tweet from? Inferring the origin locations of tweets based on contextual information](https://mlsanity.com/paper/2211.16506/) (2022)
- [Leave no Place Behind: Improved Geolocation in Humanitarian Documents](https://mlsanity.com/paper/2309.02914/) (2023)
- [A Real-time System for Detecting Landslide Reports on Social Media using Artificial Intelligence](https://mlsanity.com/paper/2202.07475/) (2022)
- [Cutting through the noise to motivate people: A comprehensive analysis of COVID-19 social media posts de/motivating vaccination](https://mlsanity.com/paper/2407.03190/) (2024)
- [Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis](https://mlsanity.com/paper/2410.03293/) (2024)
- [Learning the Topic, Not the Language: How LLMs Classify Online Immigration Discourse Across Languages](https://mlsanity.com/paper/2508.06435/) (2025)
- [LLM-Powered Automatic Translation and Urgency in Crisis Scenarios](https://mlsanity.com/paper/2602.13452/) (2026)
- [Machine Translation for Accessible Multi-Language Text Analysis](https://mlsanity.com/paper/2301.08416/) (2023)
- [WildFireCan-MMD: A Multimodal Dataset for Classification of User-Generated Content During Wildfires in Canada](https://mlsanity.com/paper/2504.13231/) (2025)
- [Extracting Disaster Impacts and Impact Related Locations in Social Media Posts Using Large Language Models](https://mlsanity.com/paper/2511.21753/) (2025)
- [CrisisKD: Five-Stage Knowledge Distillation for Aspect-Level Sentiment and Emotion Analysis in Crisis Discourse](https://mlsanity.com/paper/2609.05757/) (2026)

## Abstract (arXiv)

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.