ArticleComputer methods and programs in biomedicine update2022
Machine learning models to detect anxiety and depression through social media: A scoping review.
Article in Computer methods and programs in biomedicine update, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Text-Based Depression Prediction on Social Media Using Machine Learning: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Machine learning approaches to anxiety detection: trends, model evaluation, and future directions.Frontiers in artificial intelligence · 2025Pooled it
- Temporal Dynamics of Influenza-Associated Anxiety Symptom Linguistic Markers on Weibo (2023-2024): Observational Study.JMIR infodemiology · 2026Observational
- Mental Health Risk Detection From Social Media Text Data: A Scoping Review of the Machine Learning Research Landscape.PsyCh journal · 2026Article
- Depression detectives: piloting a methodology for online co-produced research.Research involvement and engagement · 2026Article
- Association between achievement motivation and anxiety symptoms among college students.Frontiers in psychology · 2026Article
- Predicting student mental health through entropy-based features and interpretable cross-attention transformer networks.PloS one · 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
- Directory of Public Datasets for Youth Mental Health to Enhance Research Through Data, Accessibility, and Artificial Intelligence: Scoping Review.JMIR mental health · 2025Article
- Applying language models for suicide prevention: evaluating news article adherence to WHO reporting guidelines.Npj mental health research · 2025Article
- An 81-million-word multi-genre corpus of Arabic books.Data in brief · 2025Article
- Mindfulness Intervention for Health Information Avoidance in Older Adults: Mixed Methods Study.JMIR public health and surveillance · 2025Article
- Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping Review.Journal of medical Internet research · 2024Article
- Natural Language Processing for Depression Prediction on Sina Weibo: Method Study and Analysis.JMIR mental health · 2024Article
- Computational Approaches for Connecting Maternal Stress to Preterm Birth.Clinics in perinatology · 2024Review
- Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2024Article
- Machine learning and natural language processing to assess the emotional impact of influencers' mental health content on Instagram.PeerJ. Computer science · 2024Article
- Predicting Depression, Anxiety, and Their Comorbidity among Patients with Breast Cancer in China Using Machine Learning: A Multisite Cross-Sectional Study.Depression and anxiety · 2024Article
- The youth mental health crisis: analysis and solutions.Frontiers in psychiatry · 2024Review
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite improvement in detection rates, the prevalence of mental health disorders such as anxiety and depression are on the rise especially since the outbreak of the COVID-19 pandemic. Symptoms of mental health disorders have been noted and observed on social media forums such Facebook. We explored machine learning models used to detect anxiety and depression through social media. Six bibliographic databases were searched for conducting the review following PRISMA-ScR protocol. We included 54 of 2219 retrieved studies. Users suffering from anxiety or depression were identified in the reviewed studies by screening their online presence and their sharing of diagnosis by patterns in their language and online activity. Majority of the studies (70%, 38/54) were conducted at the peak of the COVID-19 pandemic (2019-2020). The studies made use of social media data from a variety of different platforms to develop predictive models for the detection of depression or anxiety. These included Twitter, Facebook, Instagram, Reddit, Sina Weibo, and a combination of different social sites posts. We report the most common Machine Learning models identified. Identification of those suffering from anxiety and depression disorders may be achieved using prediction models to detect user's language on social media and has the potential to complimenting traditional screening. Such analysis could also provide insights into the mental health of the public especially so when access to health professionals can be restricted due to lockdowns and temporary closure of services such as we saw during the peak of the COVID-19 pandemic.
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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.