This paper is in the following e-collection/theme issue:

Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1484) Infodemiology and Infoveillance (1278) JMIR Theme Issue: COVID-19 Special Issue (2394) Machine Learning (1400) Infectious Diseases (non-STD/STI) (1490) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1015) Mass Media/Social Media Communication and Campaigns (450) Outbreak and Pandemic Preparedness and Management (1679) Infoveillance and Social Listening (181)

Published on in Vol 6, No 2 (2020): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19509, first published .
Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study

Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study

Machine Learning to Detect Self-Reporting of Symptoms, Testing Access, and Recovery Associated With COVID-19 on Twitter: Retrospective Big Data Infoveillance Study

Authors of this article:

Tim Mackey1, 2, 3, 4 Author Orcid Image ;   Vidya Purushothaman2, 5 Author Orcid Image ;   Jiawei Li1, 2, 3, 4 Author Orcid Image ;   Neal Shah1, 4 Author Orcid Image ;   Matthew Nali1, 3 Author Orcid Image ;   Cortni Bardier6 Author Orcid Image ;   Bryan Liang2, 3 Author Orcid Image ;   Mingxiang Cai2, 3, 7 Author Orcid Image ;   Raphael Cuomo1, 2 Author Orcid Image
Article Authors Cited by (99) Tweetations (26) Metrics

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