A large-scale COVID-19 Twitter chatter dataset for open scientific research - an international collaboration Article Swipe
Juan M. Banda
,
Ramya Tekumalla
,
Guanyu Wang
,
Jingyuan Yu
,
Tuo Liu
,
Yuning Ding
,
Katya Artemova
,
Elena Tutubalina
,
Gerardo Chowell
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.7482517
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.7482517
Version 146 of the dataset. MAJOR CHANGE NOTE: The dataset files: full_dataset.tsv.gz and full_dataset_clean.tsv.gz have been split in 1 GB parts using the Linux utility called Split. So make sure to join the parts before unzipping. We had to make this change as we had huge issues uploading files larger than 2GB's (hence the delay in the dataset releases). 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 (1,376,960,012 unique tweets), and a cleaned version with no retweets on the full_dataset-clean.tsv file (356,967,392 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.
Related Topics
Concepts
Coronavirus disease 2019 (COVID-19)
Scale (ratio)
Open science
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Data science
2019-20 coronavirus outbreak
Computer science
Geography
Statistics
Biology
Mathematics
Cartography
Virology
Medicine
Outbreak
Pathology
Disease
Infectious disease (medical specialty)
Metadata
- Type
- dataset
- Language
- en
- Landing Page
- https://zenodo.org/record/7482517
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4393661502
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4393661502Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5281/zenodo.7482517Digital Object Identifier
- Title
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A large-scale COVID-19 Twitter chatter dataset for open scientific research - an international collaborationWork title
- Type
-
datasetOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
-
2022-12-25Full publication date if available
- Authors
-
Juan M. Banda, Ramya Tekumalla, Guanyu Wang, Jingyuan Yu, Tuo Liu, Yuning Ding, Katya Artemova, Elena Tutubalina, Gerardo ChowellList of authors in order
- Landing page
-
https://zenodo.org/record/7482517Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
-
https://zenodo.org/record/7482517Direct OA link when available
- Concepts
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Coronavirus disease 2019 (COVID-19), Scale (ratio), Open science, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Data science, 2019-20 coronavirus outbreak, Computer science, Geography, Statistics, Biology, Mathematics, Cartography, Virology, Medicine, Outbreak, Pathology, Disease, Infectious disease (medical specialty)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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