@inproceedings{hu-etal-2020-weibo,
    title = "{W}eibo-{COV}: A Large-Scale {COVID}-19 Social Media Dataset from {W}eibo",
    author = "Hu, Yong  and
      Huang, Heyan  and
      Chen, Anfan  and
      Mao, Xian-Ling",
    editor = "Verspoor, Karin  and
      Cohen, Kevin Bretonnel  and
      Conway, Michael  and
      de Bruijn, Berry  and
      Dredze, Mark  and
      Mihalcea, Rada  and
      Wallace, Byron",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.nlpcovid19-2.34/",
    doi = "10.18653/v1/2020.nlpcovid19-2.34",
    abstract = "With the rapid development of COVID-19 around the world, people are requested to maintain {\textquotedblleft}social distance{\textquotedblright} and {\textquotedblleft}stay at home{\textquotedblright}. 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."
}