Your browser doesn't support javascript.
#COVID-19: A Public Coronavirus Twitter Dataset Tracking Social Media Discourse about the Pandemic
JMIR Public Health Surveill ; 2020.
Article | WHO COVID | ID: covidwho-333044
ABSTRACT

BACKGROUND:

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.

OBJECTIVE:

In this paper, we describe a multilingual coronavirus (COVID-19) Twitter dataset 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 API and Tweepy to follow certain keywords and accounts that were trending at the time the collection began, and 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 manuscript also presents basic analysis that shows that Twitter activity responds and reacts to coronavirus-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 dataset could also help track scientific coronavirus misinformation and unverified rumors or enable the understanding of fear and panic - and undoubtedly more. CLINICALTRIAL

Full text: Available Collection: Databases of international organizations Database: WHO COVID Type of study: Qualitative research Journal: JMIR Public Health Surveill Year: 2020 Document Type: Article

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: Databases of international organizations Database: WHO COVID Type of study: Qualitative research Journal: JMIR Public Health Surveill Year: 2020 Document Type: Article