Mental health, COVID-19, Twitter, social media, depression, suicidal ideation, loneliness, public health crisis, psychological well-being, infodemiology, machine learning framework, digital surveillance, emotional distress, online survey
Telecommunications Advancement Foundation
MU and KW conceptualized and designed the study, MU and HS designed and administered the survey, KW and MU collected and analyzed data, MU drafted the main parts of the manuscript, and KW and HS provided critical revision for important intellectual content. All authors have read and approved the manuscript.
This study was funded by the Telecommunications Advancement Foundation. The funder had no role in study design, data collection and analysis, the decision to publish, or preparation of the manuscript.
Emergency and Disaster Management | Health Policy | Policy Design, Analysis, and Evaluation | Public Administration | Public Affairs | Public Policy | Science and Technology Policy | Social Policy | Social Welfare
The brief provides a summary of "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," co-authored by Michiko Ueda-Ballmer, Kohei Watanabe, and Hajime Sueki and published in the Journal of Medical Internet Research.
Ueda-Ballmer, Michiko; Watanabe, Kohei; Sueki, Hajime; and Mondom, Davor, "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" (2023). Center for Policy Design and Governance. 2.
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Emergency and Disaster Management Commons, Health Policy Commons, Policy Design, Analysis, and Evaluation Commons, Public Administration Commons, Public Affairs Commons, Public Policy Commons, Science and Technology Policy Commons, Social Policy Commons, Social Welfare Commons