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

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
  2. Baird A, Xia Y. Applying analytics to sociodemographic disparities in mental health. Nature Mental Health 2025;3(1):124 View
  3. DuPont-Reyes M, Zou W, Li J, Villatoro A, Tang L. A machine learning language model approach to evaluating mental health awareness content across Spanish- and English-language social media posts on Twitter. Social Psychiatry and Psychiatric Epidemiology 2025 View
  4. Dilanka R, Rupasingha R. Sentiment Analysis on Suicidal Tendency Affected by the COVID -19 Pandemic: A Comparison of Different Algorithms using Twitter Data. Coronaviruses 2025;6(2) View
  5. Meneses do Rêgo A, Filho I. The influence of instagram on medical education in the age of artificial intelligence: a formal assessment of its utility in health education. Open Access Journal of Science 2024;7(1):61 View

Books/Policy Documents

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