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

Ari Klein; Arjun Magge; Karen O'Connor; Haitao Cai; Davy Weissenbacher; Graciela Gonzalez-Hernandez

The rapidly evolving outbreak of COVID-19, and the delay and shortage of available testing in the United States, presents challenges for actively monitoring its spread and preparing in response. One approach for detecting cases without the need of extensive testing relies on voluntary self-reports of symptoms from the general population. 1 However, the incubation period of COVID-19 2 may limit active monitoring based primarily on symptoms. Considering that nearly one of every four adults in the United States already uses Twitter, and nearly half of them use it on a daily basis, 3 in this proof-of-concept study, we assessed (1) whether users report personal information on Twitter that could more broadly indicate potential exposure to COVID-19, and (2) the utility of our social media mining approach for automatically detecting these users and analyzing the chronological and geographical distribution of their reports. To our knowledge, the use of real-time Twitter data to track COVID-19 4 has not extended to user-level, personal reports. Thus, our natural language processing and machine learning framework could advance the use of Twitter data as a complementary resource "to understand and model the transmission and trajectory of COVID-19". 5 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.