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Extending A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter to England, UK
- Source :
- medRxiv, article-version (status) pre, article-version (number) 1
- Publication Year :
- 2020
- Publisher :
- Cold Spring Harbor Laboratory, 2020.
-
Abstract
- The rapidly evolving COVID-19 pandemic presents challenges for actively monitoring its transmission. In this study, we extend a social media mining approach used in the US to automatically identify personal reports of COVID-19 on Twitter in England, UK. The findings indicate that natural language processing and machine learning framework could help provide an early indication of the chronological and geographical distribution of COVID-19 in England.
- Subjects :
- Coronavirus disease 2019 (COVID-19)
business.industry
05 social sciences
MEDLINE
Distribution (economics)
050801 communication & media studies
Data science
Article
law.invention
03 medical and health sciences
0302 clinical medicine
0508 media and communications
Transmission (mechanics)
Geography
Social media mining
law
Pandemic
030212 general & internal medicine
business
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- medRxiv
- Accession number :
- edsair.doi.dedup.....77aa857aa837e0b4ada6cbbebe4ec120