ReviewJMIR mental health2022
Detecting and Measuring Depression on Social Media Using a Machine Learning Approach: Systematic Review.
Review in JMIR mental health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 4 of them syntheses that pooled it.
What it found
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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.
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Who cites it
26 citing papers in PubMed, 4 syntheses or guidelines pooled it, 109 citations in OpenAlex.
- Artificial Intelligence in Diagnosing Depression Through Behavioural Cues: A Diagnostic Accuracy Systematic Review and Meta-Analysis.Journal of clinical nursing · 2026Pooled it
- Text-Based Depression Prediction on Social Media Using Machine Learning: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- An in-depth exploration of machine learning methods for mental health state detection: a systematic review and analysis.Frontiers in digital health · 2025Pooled it
- What methods are used to examine representation of mental ill-health on social media? A systematic review.BMC psychology · 2024Pooled it
- AI in Mental Health: Transforming Diagnosis and Management of Depression and Anxiety.Health science reports · 2026Article
- Interpretable depression assessment using a large language model.PLOS digital health · 2026Article
- Toward developing adolescent-centered machine learning methods to detect depression: Interviews with Latino adolescents to identify signals of emotional and somatic symptoms within social media data.PLOS digital health · 2026Article
- Using SHAP and LIME to Explain Machine Learning Models Predicting Comorbid Depression and Stroke From Daily Dietary Nutrient Intake in a US Population-Based Study.Food science & nutrition · 2026Article
- Predicting student mental health through entropy-based features and interpretable cross-attention transformer networks.PloS one · 2026Article
- Development and Validation of a Depression Scale for Online Assessment: Cross-Sectional Observational Study.Journal of medical Internet research · 2025Observational
- A randomised cross over trial examining the linguistic markers of depression and anxiety in symptomatic adults.Npj mental health research · 2025Article
- Article
- Assessing ML classification algorithms and NLP techniques for depression detection: An experimental case study.PloS one · 2025Article
- Predicting Depressive Symptoms Using GPS-Based Regional Data in Germany With the CORONA HEALTH App During the COVID-19 Pandemic: Cross-Sectional Study.Interactive journal of medical research · 2024Article
- Article
- Examining the Gateway Hypothesis and Mapping Substance Use Pathways on Social Media: Machine Learning Approach.JMIR formative research · 2024Article
- From antidepressants and psychotherapy to oxytocin, vagus nerve stimulation, ketamine and psychedelics: how established and novel treatments can improve social functioning in major depression.Frontiers in psychiatry · 2024Review
- A systematic review on automated clinical depression diagnosis.Npj mental health research · 2023Review
- Linguistic Analysis for Identifying Depression and Subsequent Suicidal Ideation on Weibo: Machine Learning Approaches.International journal of environmental research and public health · 2023Article
- Detecting depression of Chinese microblog usersFrontiers in psychiatry · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDetection of depression gained prominence soon after this troublesome disease emerged as a serious public health concern worldwide.
objectiveThis systematic review aims to summarize the findings of previous studies concerning applying machine learning (ML) methods to text data from social media to detect depressive symptoms and to suggest directions for future research in this area.
methodsA bibliographic search was conducted for the period of January 1990 to December 2020 in Google Scholar, PubMed, Medline, ERIC, PsycINFO, and BioMed. Two reviewers retrieved and independently assessed the 418 studies consisting of 322 articles identified through database searching and 96 articles identified through other sources; 17 of the studies met the criteria for inclusion.
resultsOf the 17 studies, 10 had identified depression based on researcher-inferred mental status, 5 had identified it based on users' own descriptions of their mental status, and 2 were identified based on community membership. The ML approaches of 13 of the 17 studies were supervised learning approaches, while 3 used unsupervised learning approaches; the remaining 1 study did not describe its ML approach. Challenges in areas such as sampling, optimization of approaches to prediction and their features, generalizability, privacy, and other ethical issues call for further research.
conclusionsML approaches applied to text data from users on social media can work effectively in depression detection and could serve as complementary tools in public mental health practice.
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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.