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

Pilot studies (ehealth) (255) Mental Health Issues in Adolescence (524) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1808) Artificial Intelligence (1740) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1789) Digital Biomarkers and Digital Phenotyping (229) Outbreak and Pandemic Preparedness and Management (1780)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52316, first published .
Leveraging Social Media to Predict COVID-19–Induced Disruptions to Mental Well-Being Among University Students: Modeling Study

Leveraging Social Media to Predict COVID-19–Induced Disruptions to Mental Well-Being Among University Students: Modeling Study

Leveraging Social Media to Predict COVID-19–Induced Disruptions to Mental Well-Being Among University Students: Modeling Study

Authors of this article:

Vedant Das Swain1 Author Orcid Image ;   Jingjing Ye2 Author Orcid Image ;   Siva Karthik Ramesh2 Author Orcid Image ;   Abhirup Mondal2 Author Orcid Image ;   Gregory D Abowd3 Author Orcid Image ;   Munmun De Choudhury1 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

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  1. Arifi D, Resch B, Santillana M, Guan W, Knoblauch S, Lautenbach S, Jaenisch T, Morales I, Havas C. Geosocial Media’s Early Warning Capabilities Across US County-Level Political Clusters: Observational Study. JMIR Infodemiology 2025;5:e58539 View
  2. Heckler W, Feijó L, de Carvalho J, Barbosa J. Digital phenotyping for mental health based on data analytics: A systematic literature review. Artificial Intelligence in Medicine 2025;163:103094 View

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