Extending A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter to England, UK

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 pr...

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Detalles Bibliográficos
Autores principales: Golder, S, Klein, Ari Z., Magge, Arjun, O’Connor, Karen, Cai, Haitao, Weissenbacher, Davy, Gonzalez-Hernandez, Graciela
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273260/
https://www.ncbi.nlm.nih.gov/pubmed/32511492
http://dx.doi.org/10.1101/2020.05.05.20083436
Descripción
Sumario: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.

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