Author: Klein, Ari Z.; Magge, Arjun; O’Connor, Karen M.S.; Cai, Haitao; Weissenbacher, Davy; Gonzalez-Hernandez, Graciela
Title: A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter Cord-id: 8f1arjw1 Document date: 2020_4_24
ID: 8f1arjw1
Snippet: 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.
Document: 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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