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

Pilot studies (ehealth) (247) Mental Health Issues in Adolescence (507) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (1748) Artificial Intelligence (1658) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1764) Digital Biomarkers and Digital Phenotyping (222) Outbreak and Pandemic Preparedness and Management (1776)

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 (2) Tweetations Metrics

Journals

  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