Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set
By Emily Chen, Kristina Lerman, Emilio Ferrara. JMIR Public Health and Surveillance, 2020
729 citations recorded by the source corpus.
Background At the time of this writing, the coronavirus disease (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 of the conversation about these phenomena now occurs online on social media platforms like Twitter. Objective In this paper, we describe a multilingual COVID-19 Twitter data set that we are making available to the research community via our COVID-19-TweetIDs GitHub repository. Methods We started this ongoing data collection on January 28, 2020, leveraging Twitter’s streaming application programming interface (API) and Tweepy to follow certain keywords and accounts that were trending at the time data collection began. We used Twitter’s search API to query for past tweets, resulting in the earliest tweets in our collection dating back to January 21, 2020. Results Since the inception of our collection, we have actively maintained and updated our GitHub repository on a weekly basis. We have published over 123 million tweets, with over 60% of the tweets in English. This paper also presents basic statistics that show that Twitter activity responds and reacts to COVID-19-related events. Conclusions 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 data set could also help track COVID-19-related misinformation and unverified rumors or enable the understanding of fear and panic—and undoubtedly more.
Subfields: Epidemiology, Sociology and Political Science, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/e2bda1a9c0c5263b0812a9227460db6b710c9fac
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In Sociology and Political Science
Citations in the corpus
- Mapping the Landscape of Artificial Intelligence Applications against COVID-19 (2020, background): “In an effort to find early warning signals of a country or region experiencing an info-demic, Gallotti et al. (2020) † analyze social media posts on Twitter across 64 languages.”
- Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach (2020, background): “Since the COVID-19 outbreak, a growing number of studies have collected Twitter data to understand the public responses to and discussions around COVID-19 [11-18].”