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Extending A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter to England, UK
Su Golder; Ari Klein; Arjun Magge; Karen O'Connor; Haitao Cai; Davy Weissenbacher.
Afiliação
  • Su Golder; University of York
  • Ari Klein; University of Pennsylvania
  • Arjun Magge; University of Pennsylvannia
  • Karen O'Connor; University of Pennsylvannia
  • Haitao Cai; University of Pennsylvannia
  • Davy Weissenbacher; University of Pennsylvannia
Preprint em En | PREPRINT-MEDRXIV | ID: ppmedrxiv-20083436
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.
Licença
cc_by_nd
Texto completo: 1 Coleções: 09-preprints Base de dados: PREPRINT-MEDRXIV Idioma: En Ano de publicação: 2020 Tipo de documento: Preprint
Texto completo: 1 Coleções: 09-preprints Base de dados: PREPRINT-MEDRXIV Idioma: En Ano de publicação: 2020 Tipo de documento: Preprint