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A Chronological and Geographical Analysis of Personal Reports of COVID-19 on Twitter

Klein, A.; Magge, A.; O'Connor, K.; Cai, H.; Weissenbacher, D.; Gonzalez-Hernandez, G.

2020-04-22 health informatics
10.1101/2020.04.19.20069948 medRxiv
Show 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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The top 4 journals account for 50% of the predicted probability mass.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.