The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration
Open accessAbstract
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.
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Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., Xiang, J., Wang, Y., Song, B., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30566-3 · Cited by 27,263
The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423
Pathological findings of COVID-19 associated with acute respiratory distress syndrome
Xu, Z., Shi, L., Wang, Y., Zhang, J., Huang, L., Zhang, C., Liu, S., Zhao, P., Liu, H., Zhu, L., et al. (2020) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(20)30076-x · Cited by 8,545
Strengthening Research through Data Sharing
Warren, E. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1607282 · Cited by 116
Banda, J. M., Tekumalla, R., Wang, G., Yu, J., Liu, T., Ding, Y., Artemova, K., Tutubalina, E., & Chowell, G. (2020) · Zenodo · doi:10.5281/zenodo.4130375 · Cited by 86
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Hussain, A., Tahir, A., Hussain, Z., Sheikh, Z., Gogate, M., Dashtipour, K., Ali, A., & Sheikh, A. (2021) · Journal of Medical Internet Research · doi:10.2196/26627 · Cited by 285
A systematic review on AI/ML approaches against COVID-19 outbreak
Dogan, O., Tiwari, S., Jabbar, M. A., & Guggari, S. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00424-8 · Cited by 87
Barnes, S. J. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106967 · Cited by 23
Metadata
- Authors
- Banda, J. M., Tekumalla, R., Wang, G., Yu, J., Liu, T., Ding, Y., Artemova, E., Tutubalina, E., & Chowell, G.
- Venue
- Epidemiologia
- Publisher
- MDPI AG
- Published
- 2021-08-05
- Volume
- 2
- Issue
- 3
- Pages
- 315-324
- ISSN
- 2673-3986 (ISSN-L)
- OpenAlex
- W3192600070
- Cited by
- 291
- References
- 5
- FWCI
- 79.90
- Type
- journal article
- Language
- en
- Open access
- gold
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- Banda, J. M., Tekumalla, R., Wang, G., Yu, J., Liu, T., Ding, Y., Artemova, E., Tutubalina, E., & Chowell, G. (2021). A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration. Epidemiologia. https://doi.org/10.3390/epidemiologia2030024
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