ReviewNPJ digital medicine2020
Methods in predictive techniques for mental health status on social media: a critical review.
Review in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 124 papers, 8 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
124 citing papers in PubMed, 8 syntheses or guidelines pooled it, 493 citations in OpenAlex.
- Review: Social media use and adolescent mental health - an umbrella review and power analysis.Child and adolescent mental health · 2026Pooled it
- AI-Driven Mental Health Support for Caregivers of Individuals With Alzheimer Disease: Systematic Literature Review and Development of a Conceptual Framework.JMIR mental health · 2026Pooled it
- Text-Based Depression Prediction on Social Media Using Machine Learning: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- What methods are used to examine representation of mental ill-health on social media? A systematic review.BMC psychology · 2024Pooled it
- Health Care Professionals' Views on the Use of Passive Sensing, AI, and Machine Learning in Mental Health Care: Systematic Review With Meta-Synthesis.JMIR mental health · 2024Pooled it
- Natural language processing for mental health interventions: a systematic review and research framework.Translational psychiatry · 2023Pooled it
- Methodologies for Monitoring Mental Health on Twitter: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Just-in-Time Adaptive Mechanisms of Popular Mobile Apps for Individuals With Depression: Systematic App Search and Literature Review.Journal of medical Internet research · 2021Pooled it
- Academic Stress, Family Relationships, and Social Media Use Disorder Among Turkish Middle School Students: Explanatory Sequential Mixed Methods Study.Journal of medical Internet research · 2026Article
- Emotional Expression and Mental Health Support in BTS Fandom Communities Using Natural Language Processing on YouTube Comments: Cross-Sectional Study.JMIR infodemiology · 2026Article
- Artificial Intelligence in Psychiatry: Five Decades of Progress and Persistent Translational Challenges.Psychiatric research and clinical practice · 2026Article
- Benchmarking large language models against practicing clinicians on psychopathological assessment.NPJ digital medicine · 2026Article
- Pain-centered symptom-language co-occurrence networks differ between interstitial cystitis/bladder pain syndrome and fibromyalgia in Reddit patient discourse.Scientific reports · 2026Article
- Article
- Predicting Momentary Suicidal Ideation From Smartphone Screenshots Using Vision-Language Models: Prospective Machine Learning Study.JMIR mental health · 2026Article
- Exploring approaches to computational representation and classification of user-generated meal logs.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Cross platform social media analysis for mental health detection.Discover mental health · 2026Review
- The Structure of Psychopathology on Reddit: Network Analysis of Mental Health Communities in Relation to the ICD Diagnostic System.Journal of medical Internet research · 2026Article
- Exploiting topic analysis models to explore psychological dimensions in social media data.Scientific reports · 2026Article
- Triaging Casual From Critical-Leveraging Machine Learning to Detect Self-Harm and Suicide Risks for Youth on Social Media: Algorithm Development and Validation Study.JMIR mental health · 2026Article
64 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Social media is now being used to model mental well-being, and for understanding health outcomes. Computer scientists are now using quantitative techniques to predict the presence of specific mental disorders and symptomatology, such as depression, suicidality, and anxiety. This research promises great benefits to monitoring efforts, diagnostics, and intervention design for these mental health statuses. Yet, there is no standardized process for evaluating the validity of this research and the methods adopted in the design of these studies. We conduct a systematic literature review of the state-of-the-art in predicting mental health status using social media data, focusing on characteristics of the study design, methods, and research design. We find 75 studies in this area published between 2013 and 2018. Our results outline the methods of data annotation for mental health status, data collection and quality management, pre-processing and feature selection, and model selection and verification. Despite growing interest in this field, we identify concerning trends around construct validity, and a lack of reflection in the methods used to operationalize and identify mental health status. We provide some recommendations to address these challenges, including a list of proposed reporting standards for publications and collaboration opportunities in this interdisciplinary space.
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.