SynthesisJournal of medical Internet research2017
Researching Mental Health Disorders in the Era of Social Media: Systematic Review.
Synthesis in Journal of medical Internet research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers, 9 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
86 citing papers in PubMed, 9 syntheses or guidelines pooled it, 276 citations in OpenAlex.
- Ethical issues raised by artificial intelligence and big data in population health: a scoping review.Frontiers in sociology · 2025Pooled it
- Artificial intelligence in the care of children and adolescents with chronic diseases: a systematic review.European journal of pediatrics · 2024Pooled it
- What methods are used to examine representation of mental ill-health on social media? A systematic review.BMC psychology · 2024Pooled it
- Methodologies for Monitoring Mental Health on Twitter: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Machine Learning and Natural Language Processing in Mental Health: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Digital Data Sources and Their Impact on People's Health: A Systematic Review of Systematic Reviews.Frontiers in public health · 2021Pooled it
- Toward an Ethical Framework for the Text Mining of Social Media for Health Research: A Systematic Review.Frontiers in digital health · 2020Pooled it
- Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review.Journal of medical Internet research · 2018Pooled it
- Data Mining Algorithms and Techniques in Mental Health: A Systematic Review.Journal of medical systems · 2018Pooled it
- Computer-Assisted Performance-Based Assessment for Mental Health: A Scoping Review.PsyCh journal · 2026Article
- Why aggregated data falls short: an exploratory survey study on barriers and facilitators in implementing the stepped care model for mental health in primary care.BMC primary care · 2026Article
- Artificial intelligence in thyroid ultrasound: clinical applications and perspectives.Frontiers in endocrinology · 2026Review
- Narrating anxiety as "ordinary": digital self-healing diaries and the normalization of anxiety on Chinese social media.Frontiers in psychology · 2026Article
- Smartphone use in a large US adult population: Temporal associations between objective measures of usage and mental well-being.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Infectious Disease Surveillance in the Era of Big Data and AI: Opportunities and Pitfalls.Cureus · 2025Review
- Pathological Processes Among Content Creators on Social Media: Scoping Review.JMIR public health and surveillance · 2025Article
- Unraveling Online Mental Health Through the Lens of Early Maladaptive Schemas: AI-Enabled Content Analysis of Online Mental Health Communities.Journal of medical Internet research · 2025Article
- Applications of Large Language Models in the Field of Suicide Prevention: Scoping Review.Journal of medical Internet research · 2025Article
- Social media as a workplace panopticon: The development and validation of social media monitoring by workplace contacts scale.PloS one · 2025Article
- Detecting and tracking depression through temporal topic modeling of tweets: insights from a 180-day study.Npj mental health research · 2024Article
26 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
3 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMental illness is quickly becoming one of the most prevalent public health problems worldwide. Social network platforms, where users can express their emotions, feelings, and thoughts, are a valuable source of data for researching mental health, and techniques based on machine learning are increasingly used for this purpose.
objectiveThe objective of this review was to explore the scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research.
methodsWe performed a systematic literature review in March 2017, using keywords to search articles on data mining of social network data in the context of common mental health disorders, published between 2010 and March 8, 2017 in medical and computer science journals.
resultsThe initial search returned a total of 5386 articles. Following a careful analysis of the titles, abstracts, and main texts, we selected 48 articles for review. We coded the articles according to key characteristics, techniques used for data collection, data preprocessing, feature extraction, feature selection, model construction, and model verification. The most common analytical method was text analysis, with several studies using different flavors of image analysis and social interaction graph analysis.
conclusionsDespite an increasing number of studies investigating mental health issues using social network data, some common problems persist. Assembling large, high-quality datasets of social media users with mental disorder is problematic, not only due to biases associated with the collection methods, but also with regard to managing consent and selecting appropriate analytics techniques.
Indexed as
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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.