ReviewHealthcare (Basel, Switzerland)2022
Detecting Depression Signs on Social Media: A Systematic Literature Review.
Review in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed, 2 syntheses or guidelines pooled it, 67 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
- Depressive Symptoms Are Associated with Linguistic Features of Negative but Not Positive Autobiographical Narratives.Affective science · 2026Article
- AI in Mental Health: Transforming Diagnosis and Management of Depression and Anxiety.Health science reports · 2026Article
- Construction of a transfer learning-based depression detection model for female breast cancer patients: text sentiment analysis.BMC cancer · 2025Article
- Applying language models for suicide prevention: evaluating news article adherence to WHO reporting guidelines.Npj mental health research · 2025Article
- The FAIIR conversational AI agent assistant for youth mental health service provision.NPJ digital medicine · 2025Article
- Explosion of research on psychopathology and social media use after COVID-19: A scientometric study.World journal of psychiatry · 2024Article
- Depression Detection on Social Media: A Classification Framework and Research Challenges and Opportunities.Journal of healthcare informatics research · 2024Article
- Decoding Wilson disease: a machine learning approach to predict neurological symptoms.Frontiers in neurology · 2024Article
- A systematic review on automated clinical depression diagnosis.Npj mental health research · 2023Review
- Detection of Depression-Related Tweets in Mexico Using Crosslingual Schemes and Knowledge Distillation.Healthcare (Basel, Switzerland) · 2023Article
- Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review.Cognitive neurodynamics · 2022Review
- Factors Affecting the Use of Social Networks and Its Effect on Anxiety and Depression among Parents and Their Children: Predictors Using ML, SEM and Extended TAM.International journal of environmental research and public health · 2022Article
- Detecting Suicidal Ideation in Social Media: An Ensemble Method Based on Feature Fusion.International journal of environmental research and public health · 2022Article
- An optimized deep learning approach for suicide detection through Arabic tweets.PeerJ. Computer science · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Among mental health diseases, depression is one of the most severe, as it often leads to suicide; due to this, it is important to identify and summarize existing evidence concerning depression sign detection research on social media using the data provided by users. This review examines aspects of primary studies exploring depression detection from social media submissions (from 2016 to mid-2021). The search for primary studies was conducted in five digital libraries: ACM Digital Library, IEEE Xplore Digital Library, SpringerLink, Science Direct, and PubMed, as well as on the search engine Google Scholar to broaden the results. Extracting and synthesizing the data from each paper was the main activity of this work. Thirty-four primary studies were analyzed and evaluated. Twitter was the most studied social media for depression sign detection. Word embedding was the most prominent linguistic feature extraction method. Support vector machine (SVM) was the most used machine-learning algorithm. Similarly, the most popular computing tool was from Python libraries. Finally, cross-validation (CV) was the most common statistical analysis method used to evaluate the results obtained. Using social media along with computing tools and classification methods contributes to current efforts in public healthcare to detect signs of depression from sources close to patients.
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.