# Weibo-COV: A Large-Scale COVID-19 Social Media Dataset from Weibo

arXiv: https://arxiv.org/abs/2005.09174 · HTML: https://mlsanity.com/paper/2005.09174/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2020.

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

- [Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning](https://mlsanity.com/paper/2204.08143/) (2022)
- [SPEED++: A Multilingual Event Extraction Framework for Epidemic Prediction and Preparedness](https://mlsanity.com/paper/2410.18393/) (2024)
- [Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)](https://mlsanity.com/paper/2204.09952/) (2022)
- [A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection](https://mlsanity.com/paper/2304.01492/) (2023)
- [Nested Named-Entity Recognition on Vietnamese COVID-19: Dataset and Experiments](https://mlsanity.com/paper/2504.21016/) (2025)
- [Research on Short-Video Platform User Decision-Making via Multimodal Temporal Modeling and Reinforcement Learning](https://mlsanity.com/paper/2509.12269/) (2025)
- [The Art of Storytelling in Authoritarian Regimes: Crafting State Narratives on Chinese Social Media](https://mlsanity.com/paper/2512.11875/) (2025)
- [Large language models for spreading dynamics in complex systems](https://mlsanity.com/paper/2602.08085/) (2026)
- [SURGE: An Event-Centric Social Media Sentiment Time Series Benchmark with Interaction Structure](https://mlsanity.com/paper/2605.21198/) (2026)
- [Against Political Polarization: A Unified Framework for Tracing Evolving Political Ideologies on Social Media](https://mlsanity.com/paper/2608.17987/) (2026)
- [Caught in the Story: Narrative Captivity in Multi-turn LLMs Conversation](https://mlsanity.com/paper/2609.03407/) (2026)

## Abstract (arXiv)

With the rapid development of COVID-19 around the world, people are requested to maintain "social distance" and "stay at home". In this scenario, extensive social interactions transfer to cyberspace, especially on social media platforms like Twitter and Sina Weibo. People generate posts to share information, express opinions and seek help during the pandemic outbreak, and these kinds of data on social media are valuable for studies to prevent COVID-19 transmissions, such as early warning and outbreaks detection. Therefore, in this paper, we release a novel and fine-grained large-scale COVID-19 social media dataset collected from Sina Weibo, named Weibo-COV, contains more than 40 million posts ranging from December 1, 2019 to April 30, 2020. Moreover, this dataset includes comprehensive information nuggets like post-level information, interactive information, location information, and repost network. We hope this dataset can promote studies of COVID-19 from multiple perspectives and enable better and rapid researches to suppress the spread of this pandemic.