mlsanityData to 14 Oct 2026

Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set

arXiv 2003.07372 ↗First posted 16 Mar 2020cs.SI q-bio.PEcited by 7

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Abstract (arXiv)

At the time of this writing, the novel coronavirus (COVID-19) pandemic outbreak has already put tremendous strain on many countries' citizens, resources and economies around the world. Social distancing measures, travel bans, self-quarantines, and business closures are changing the very fabric of societies worldwide. With people forced out of public spaces, much conversation about these phenomena now occurs online, e.g., on social media platforms like Twitter. In this paper, we describe a multilingual coronavirus (COVID-19) Twitter dataset that we have been continuously collecting since January 22, 2020. We are making our dataset available to the research community (https://github.com/echen102/COVID-19-TweetIDs). It is our hope that our contribution will enable the study of online conversation dynamics in the context of a planetary-scale epidemic outbreak of unprecedented proportions and implications. This dataset could also help track scientific coronavirus misinformation and unverified rumors, or enable the understanding of fear and panic -- and undoubtedly more. Ultimately, this dataset may contribute towards enabling informed solutions and prescribing targeted policy interventions to fight this global crisis.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Mar 2020.

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Citing papers per year: 2021: 4; 2022: 2; 2023: 0; 2024: 1.

7 papers.