A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research-An International Collaboration
Banda, Juan M. (Georgia State University (Atlanta). Department of Computer Science)
Tekumalla, Ramya (Georgia State University (Atlanta). Department of Computer Science)
Wang, Guanyu (University of Missouri. Missouri School of Journalism)
Yu, Jingyuan (Universitat Autònoma de Barcelona. Departament de Psicologia Social)
Liu, Tuo (Department of Psychology, Carl von Ossietzky Universität Oldenburg, 26129 Oldenburg, Germany)
Ding, Yuning (Universität Duisburg-Essen. Language Technology Lab)
Artemova, Ekaterina (Higher School of Economics-National Research University (Moscou, Rússia))
Tutubalina, Elena (Kazan Federal University (Kazan, Rússia). Faculty of Chemistry)
Chowell, Gerardo (Georgia State University (Atlanta). Department of Population Health Sciences)

Data:	2021
Resum:	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.
Nota:	Ajuts: This work was partially supported by the National Institute of Aging through Stanford University's Stanford Aging and Ethnogeriatrics Transdisciplinary Collaborative Center (SAGE) center (award 3P30AG059307-02S1). The work on the collection of Russian tweets was performed by Elena Tutubalina and supported by the Russian Science Foundation (grant number 18-11-00284).
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Public datasets ; Open science ; COVID-19 ; Social media ; Data sources
Publicat a:	Epidemiologia, Vol. 2 (august 2021) , p. 315-324, ISSN 2673-3986

DOI: 10.3390/epidemiologia2030024
PMID: 36417228

10 p, 631.8 KB

El registre apareix a les col·leccions:
Articles > Articles de recerca
Articles > Articles publicats