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

JMIR Theme Issue: COVID-19 Special Issue (2394) Outbreak and Pandemic Preparedness and Management (1679) Machine Learning (1400) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1015) Anxiety and Stress Disorders (1013) JMH Theme Issue: COVID-19 and Mental Health: Impact and Interventions (374) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1573) Infodemiology and Infoveillance (1278)

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

Journals

  1. Huang A, Huang S. Exploring Depression and Nutritional Covariates Amongst US Adults using Shapely Additive Explanations. Health Science Reports 2023;6(10) View

Books/Policy Documents

  1. Thakur N, Cho H, Cheng H, Lee H. HCI International 2023 – Late Breaking Papers. View