Machine Learning for Mental Health in Social Media: Bibliometric Study

Jina Kim · Daeun Lee · Eunil Park

134 citations0/0 files storedJournal of Medical Internet Research

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Mental Health via Writing Sentiment Analysis and Opinion Mining Digital Mental Health Interventions

Abstract

BACKGROUND: Social media platforms provide an easily accessible and time-saving communication approach for individuals with mental disorders compared to face-to-face meetings with medical providers. Recently, machine learning (ML)-based mental health exploration using large-scale social media data has attracted significant attention. OBJECTIVE: We aimed to provide a bibliometric analysis and discussion on research trends of ML for mental health in social media. METHODS: Publications addressing social media and ML in the field of mental health were retrieved from the Scopus and Web of Science databases. We analyzed the publication distribution to measure productivity on sources, countries, institutions, authors, and research subjects, and visualized the trends in this field using a keyword co-occurrence network. The research methodologies of previous studies with high citations are also thoroughly described. RESULTS: We obtained a total of 565 relevant papers published from 2015 to 2020. In the last 5 years, the number of publications has demonstrated continuous growth with Lecture Notes in Computer Science and Journal of Medical Internet Research as the two most productive sources based on Scopus and Web of Science records. In addition, notable methodological approaches with data resources presented in high-ranking publications were investigated. CONCLUSIONS: The results of this study highlight continuous growth in this research area. Moreover, we retrieved three main discussion points from a comprehensive overview of highly cited publications that provide new in-depth directions for both researchers and practitioners.

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References

2008Assessing Risk of Bias in Included Studies 2014A scoping review of scoping reviews: advancing the approach and enhancing the consistency 2020Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice 2016Social Networking Sites, Depression, and Anxiety: A Systematic Review 2020Methods in predictive techniques for mental health status on social media: a critical review 2017Mental Health Mobile Apps for Preadolescents and Adolescents: A Systematic Review 2018Natural Language Processing of Social Media as Screening for Suicide Risk 1989Participatory Action Research

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2021Bibliometric Studies and the Discipline of Social Media Mental Health Research. Comment on “Machine Learning for Mental Health in Social Media: Bibliometric Study”2022Research on Health Disparities Related to the COVID-19 Pandemic: A Bibliometric Analysis 2025The application of artificial intelligence in insomnia, anxiety, and depression: A bibliometric analysis 2025How to use social media and artificial intelligence to promote mental health among Chinese and Chinese American college students in the U.S.

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