With the rapid development of COVID-19, people are asked to maintain "social distance" and "stay at home". In this scenario, more and more social interactions move online, especially on social network like Twitter and Weibo. These tweets on social media play an important role in sharing information, express opinion, seek support and help during the epidemic. To facilitate the social media studies of COVID-19 and enable better and faster researches to combat the spread of this disease , we release Weibo-COV, a first large-scale COVID-19 related tweets dataset from Weibo, covering more than 30 million tweets from 1 November 2019 t0 30 April 2020. Moreover, the fields of tweets in the dataset is very rich, including basic tweets information, interactive information, location information and retweet network  .
With the rapid development of COVID-19, people are asked to maintain "social distance" and "stay at home". In this scenario, more and more social interactions move online, especially on social media like Twitter and Weibo. People post tweets to share information, express opinions and seek help during the pandemic, and these tweets on social media are valuable for studies against COVID-19 , such as disease surveillance. Therefore, in this paper, we release Weibo-COV, a first large-scale COVID-19 social media dataset from Weibo, covering more than 30 million tweets from 1 November 2019 to 30 April 2020. Moreover, the field information of the dataset is very rich, including basic tweets information, interactive information, location information and retweet network . We hope this dataset can promote studies of COVID-19 from multiple perspectives and enable better and faster researches to combat the spread of this disease .
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These tweets on social media play an important role in sharing
People post tweets to share
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opinion, seek support and
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epidemic. To facilitate the social media studies of
pandemic, and these tweets on social media are valuable for studies against
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and enable better and faster researches to combat the spread of this disease ,
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social media
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fields of tweets in
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. We hope this dataset can promote studies of COVID-19 from multiple perspectives and enable better and faster researches to combat the spread of this disease