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A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter
Preprint
in English
| medRxiv
| ID: ppmedrxiv-20069948
ABSTRACT
The rapidly evolving outbreak of COVID-19 presents challenges for actively monitoring its spread. In this study, we assessed a social media mining approach for automatically analyzing the chronological and geographical distribution of users in the United States reporting personal information related to COVID-19 on Twitter. The results suggest that our natural language processing and machine learning framework could help provide an early indication of the spread of COVID-19.
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Full text:
Available
Collection:
Preprints
Database:
medRxiv
Language:
English
Year:
2020
Document type:
Preprint
