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Published February 20, 2022 | Version 102

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

  • 1. Georgia State University
  • 2. University of Missouri
  • 3. Universitat Autònoma de Barcelona
  • 4. Carl von Ossietzky Universität Oldenburg
  • 5. Universität Duisburg-Essen
  • 6. NRU HSE
  • 7. KFU

Description

Version 102 of the dataset. The peer-reviewed publication for this dataset has now been published  in Epidemiologia an MDPI journal, and can be accessed here: https://doi.org/10.3390/epidemiologia2030024. Please cite this when using the dataset.

Due to the relevance of the COVID-19 global pandemic, we are releasing our dataset of tweets acquired from the Twitter Stream related to COVID-19 chatter. Since our first release we have received additional data from our new collaborators, allowing this resource to grow to its current size. Dedicated data gathering started from March 11th yielding over 4 million tweets a day. We have added additional data provided by our new collaborators from January 27th to March 27th, to provide extra longitudinal coverage. Version 10 added ~1.5 million tweets in the Russian language collected between January 1st and May 8th, gracefully provided to us by: Katya Artemova (NRU HSE) and Elena Tutubalina (KFU). From version 12 we have included daily hashtags, mentions and emoijis and their frequencies the respective zip files. From version 14 we have included the tweet identifiers and their respective language for the clean version of the dataset. Since version 20 we have included language and place location for all tweets.

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.tsv file (1,314,469,053 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (338,629,423 unique tweets). 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 in frequent_terms.csv, the top 1000 bigrams in frequent_bigrams.csv, and the top 1000 trigrams in frequent_trigrams.csv. Some general statistics per day are included for both datasets in the full_dataset-statistics.tsv and full_dataset-clean-statistics.tsv files. For more statistics and some visualizations visit: http://www.panacealab.org/covid19/ 

More details can be found (and will be updated faster at: https://github.com/thepanacealab/covid19_twitter) and our pre-print about the dataset (https://arxiv.org/abs/2004.03688) 

As always, the tweets distributed here are only tweet identifiers (with date and time added) due to the terms and conditions of Twitter to re-distribute Twitter data ONLY for research purposes. They need to be hydrated to be used.

Notes

This dataset will be updated bi-weekly at least with additional tweets, look at the github repo for these updates. Release: We have standardized the name of the resource to match our pre-print manuscript and to not have to update it every week.

Files

emojis.zip

Files (15.0 GB)

Name Size
md5:5c0e93de6377819935597bb77231d4f9
11.7 MB Preview Download
md5:931986ec72afd62fa2bd0ecf87dd40fa
18.5 kB Preview Download
md5:1918b4d44bb0d651a96df6e0a3650bba
11.6 kB Preview Download
md5:bc05731a8d8f16e957135d022c88bb35
25.4 kB Preview Download
md5:0fe6bac1fdcad4a20013d67b2605b621
14.5 kB Download
md5:78b5bd9b57d33e405f54caaced8132a0
11.2 GB Download
md5:1a84280999bf71088f17ffec00dfd00f
14.0 kB Download
md5:8b76fe8f72a694645a40d5f7b7188b95
3.2 GB Download
md5:fafab70b40442040e379b95cbe3fba93
184.4 MB Preview Download
md5:bb682fa4958b573d4f1610ddeb6c723f
307.1 MB Preview Download

Additional details

Related works

Is continued by
Other: http://www.panacealab.org/covid19/ (URL)
Is supplement to
Preprint: https://arxiv.org/abs/2004.03688 (URL)