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

JMIR Theme Issue: COVID-19 Special Issue (2452) Outbreak and Pandemic Preparedness and Management (1727) Machine Learning (1465) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Anxiety and Stress Disorders (1053) JMH Theme Issue: COVID-19 and Mental Health: Impact and Interventions (383) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Infodemiology and Infoveillance (1349)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44965, first published .
Emotional Distress During COVID-19 by Mental Health Conditions and Economic Vulnerability: Retrospective Analysis of Survey-Linked Twitter Data With a Semisupervised Machine Learning Algorithm

Emotional Distress During COVID-19 by Mental Health Conditions and Economic Vulnerability: Retrospective Analysis of Survey-Linked Twitter Data With a Semisupervised Machine Learning Algorithm

Emotional Distress During COVID-19 by Mental Health Conditions and Economic Vulnerability: Retrospective Analysis of Survey-Linked Twitter Data With a Semisupervised Machine Learning Algorithm

Authors of this article:

Michiko Ueda1, 2 Author Orcid Image ;   Kohei Watanabe3 Author Orcid Image ;   Hajime Sueki4 Author Orcid Image
Article Authors Cited by (2) Tweetations (22) Metrics

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