A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration
As the COVID-19 pandemic continues to spread worldwide, an unprecedented amount of open data is being generated for medical, genetics, and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is all...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-08-01
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| Series: | Epidemiologia |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-3986/2/3/24 |
| _version_ | 1797519340191350784 |
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| author | Juan M. Banda Ramya Tekumalla Guanyu Wang Jingyuan Yu Tuo Liu Yuning Ding Ekaterina Artemova Elena Tutubalina Gerardo Chowell |
| author_facet | Juan M. Banda Ramya Tekumalla Guanyu Wang Jingyuan Yu Tuo Liu Yuning Ding Ekaterina Artemova Elena Tutubalina Gerardo Chowell |
| author_sort | Juan M. Banda |
| collection | DOAJ |
| description | As the COVID-19 pandemic continues to spread worldwide, an unprecedented amount of open data is being generated for medical, genetics, and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated on the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique worldwide event in biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 1.12 billion tweets, growing daily, related to COVID-19 chatter generated from 1 January 2020 to 27 June 2021 at the time of writing. This data source provides a freely available additional data source for researchers worldwide to conduct a wide and diverse number of research projects, such as epidemiological analyses, emotional and mental responses to social distancing measures, the identification of sources of misinformation, stratified measurement of sentiment towards the pandemic in near real time, among many others. |
| first_indexed | 2024-03-10T07:41:28Z |
| format | Article |
| id | doaj.art-498e8095f708404c975307c76dd95998 |
| institution | Directory Open Access Journal |
| issn | 2673-3986 |
| language | English |
| last_indexed | 2024-03-10T07:41:28Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Epidemiologia |
| spelling | doaj.art-498e8095f708404c975307c76dd959982023-11-22T12:59:00ZengMDPI AGEpidemiologia2673-39862021-08-012331532410.3390/epidemiologia2030024A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International CollaborationJuan M. Banda0Ramya Tekumalla1Guanyu Wang2Jingyuan Yu3Tuo Liu4Yuning Ding5Ekaterina Artemova6Elena Tutubalina7Gerardo Chowell8Department of Computer Science, Georgia State University, Atlanta, GA 30303, USADepartment of Computer Science, Georgia State University, Atlanta, GA 30303, USAMissouri School of Journalism, University of Missouri, Columbia, MO 65201, USADepartment of Social Psychology, Universitat Autònoma de Barcelona, 08035 Barcelona, SpainDepartment of Psychology, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, GermanyLanguage Technology Lab, Universität Duisburg-Essen, 47057 Duisburg, GermanyFaculty of Computer Science, Higher School of Economics—National Research University, 101000 Moscow, RussiaFaculty of Chemistry, Kazan Federal University, 420008 Kazan, RussiaDepartment of Population Health Sciences, Georgia State University, Atlanta, GA 30303, USAAs the COVID-19 pandemic continues to spread worldwide, an unprecedented amount of open data is being generated for medical, genetics, and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated on the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique worldwide event in biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 1.12 billion tweets, growing daily, related to COVID-19 chatter generated from 1 January 2020 to 27 June 2021 at the time of writing. This data source provides a freely available additional data source for researchers worldwide to conduct a wide and diverse number of research projects, such as epidemiological analyses, emotional and mental responses to social distancing measures, the identification of sources of misinformation, stratified measurement of sentiment towards the pandemic in near real time, among many others.https://www.mdpi.com/2673-3986/2/3/24public datasetsopen scienceCOVID-19social mediadata sources |
| spellingShingle | Juan M. Banda Ramya Tekumalla Guanyu Wang Jingyuan Yu Tuo Liu Yuning Ding Ekaterina Artemova Elena Tutubalina Gerardo Chowell A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration Epidemiologia public datasets open science COVID-19 social media data sources |
| title | A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration |
| title_full | A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration |
| title_fullStr | A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration |
| title_full_unstemmed | A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration |
| title_short | A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration |
| title_sort | large scale covid 19 twitter chatter dataset for open scientific research an international collaboration |
| topic | public datasets open science COVID-19 social media data sources |
| url | https://www.mdpi.com/2673-3986/2/3/24 |
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