ReviewJournal of medical Internet research2021
Machine Learning for Mental Health in Social Media: Bibliometric Study.
Review in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Unleashing the potential of chatbots in mental health: bibliometric analysis.Frontiers in psychiatry · 2025Pooled it
- Methodologies for Monitoring Mental Health on Twitter: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Restorative effects of plant landscapes under different weather and phenology conditions: evidence from EEG and psychological responses.Scientific reports · 2026Article
- Thematic Mapping and Evolution of Social Media Mining in Health Research: Hybrid Bibliometric Synthesis.Journal of medical Internet research · 2026Review
- iGraphCTC: an inter-connected graph convolutional network for comprehensive clinical trial collaborations.Scientific reports · 2026Article
- Deep learning for autism detection using clinical notes: A comparison of transfer learning for a transparent and black-box approach.Artificial intelligence in medicine · 2026Article
- Understanding mental health discourse on Reddit with transformers and explainability.Scientific reports · 2026Article
- Analysis and Mapping of Machine Learning in the Context of Diabetes.Health science reports · 2025Article
- Machine learning-based predictive modelling of mental health in Rwandan Youth.Scientific reports · 2025Article
- What Is the Current Status of Research on the Impact of Digitalization in Medicine? A Bibliometric Analysis.Healthcare (Basel, Switzerland) · 2025Article
- The Current Research Landscape on the Machine Learning Application in Autism Spectrum Disorder: A Bibliometric Analysis From 1999 to 2023.Current neuropharmacology · 2025Article
- Health Locus of Control and Medical Behavioral Interventions: Systematic Review and Recommendations.Interactive journal of medical research · 2024Review
- Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study.Journal of medical Internet research · 2023Article
- Characteristics and Emerging Trends in Research on Rehabilitation Robots from 2001 to 2020: Bibliometric Study.Journal of medical Internet research · 2023Article
- New chaos-integrated improved grey wolf optimization based models for automatic detection of depression in online social media and networks.PeerJ. Computer science · 2023Article
- Longitudinal Changes of COVID-19 Symptoms in Social Media: Observational Study.Journal of medical Internet research · 2022Observational
- The Public Health Governance of the COVID-19 Pandemic: A Bibliometric Analysis.Healthcare (Basel, Switzerland) · 2022Article
- Research on Health Disparities Related to the COVID-19 Pandemic: A Bibliometric Analysis.International journal of environmental research and public health · 2022Article
- Bibliometric Studies and the Discipline of Social Media Mental Health Research. Comment on "Machine Learning for Mental Health in Social Media: Bibliometric Study".Journal of medical Internet research · 2021Article
- Authors' Reply to: Bibliometric Studies and the Discipline of Social Media Mental Health Research. Comment on "Machine Learning for Mental Health in Social Media: Bibliometric Study".Journal of medical Internet research · 2021Article
Corrections and comments
- Commented on by
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSocial 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.
objectiveWe aimed to provide a bibliometric analysis and discussion on research trends of ML for mental health in social media.
methodsPublications 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.
resultsWe 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.
conclusionsThe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.