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A large-scale COVID-19 Twitter chatter dataset for open scientific research


tags: social media, twitter, nlp, covid-19, covid19, twitter, covid, open-source



timeframe: 2000-2018

description: Dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. The first 9 weeks of data (from January 1st, 2020 to March 11th, 2020) contain very low tweet counts as we filtered other data we were collecting for other research purposes, however, one can see the dramatic increase as the awareness for the virus spread. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. The data collected from the stream captures all languages, but the higher prevalence are: English, Spanish, and French. We release all tweets and retweets on the full dataset, and a cleaned version with no retweets. There are several practical reasons for us to leave the retweets, tracing important tweets and their dissemination is one of them. For NLP tasks we provide the top 1000 frequent terms, the top 1000 bigrams, and the top 1000 trigrams. Some general statistics per day are included for both datasets.

last edit: Mon, 19 Jun 2023 16:41:37 GMT