Extending A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter to England, UK
Golder, S.; Klein, A.; Magge, A.; O'Connor, K.; Cai, H.; Weissenbacher, D.
Show 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.
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