ArticleSensors (Basel, Switzerland)2021
Schizophrenia Detection Using Machine Learning Approach from Social Media Content.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 73 citations in OpenAlex.
- Use of Social Media Data to Diagnose and Monitor Psychotic Disorders: Systematic Review.Journal of medical Internet research · 2022Pooled it
- Explainable AI based ensemble model for the identification of Schizophrenia prodromal phase.Scientific reports · 2026Article
- A validated Mandarin Chinese Auditory Emotion Database of Subject-Personal-Pronoun Sentences (MCAE-SPPS).Scientific data · 2026Article
- On the interface between linguistics, computer science and psychiatry: analyzing textual key-factors affecting BERT-based classification of schizophrenia in social media texts.Frontiers in artificial intelligence · 2026Article
- Decoding the mind of self-compassion through a topic modeling analysis of 9000+ free-text narratives.Scientific reports · 2025Article
- Estimating the Prevalence of Schizophrenia in the General Population of Japan Using an Artificial Neural Network-Based Schizophrenia Classifier: Web-Based Cross-Sectional Survey.JMIR formative research · 2025Article
- Natural Language Processing and Schizophrenia: A Scoping Review of Uses and Challenges.Journal of personalized medicine · 2024Article
- Reduced speech coherence in psychosis-related social media forum posts.Schizophrenia (Heidelberg, Germany) · 2024Article
- External Validation of a Machine Learning Model for Schizophrenia Classification.Journal of clinical medicine · 2024Article
- Mental illness detection through harvesting social media: a comprehensive literature review.PeerJ. Computer science · 2024Article
- Understanding Mental Health Issues in Different Subdomains of Social Networking Services: Computational Analysis of Text-Based Reddit Posts.Journal of medical Internet research · 2023Article
- Classifying Schizophrenia Cases by Artificial Neural Network Using Japanese Web-Based Survey Data: Case-Control Study.JMIR formative research · 2023Article
- Machine learning techniques in diagnostics and prediction of the clinical features of schizophrenia: a narrative review.Consortium psychiatricum · 2023Review
- Emergence of Language Related to Self-experience and Agency in Autobiographical Narratives of Individuals With Schizophrenia.Schizophrenia bulletin · 2023Article
- Grammatical impairment in schizophrenia: An exploratory study of the pronominal and sentential domains.PloS one · 2023Article
- The use of artificial intelligence for delivery of essential health services across WHO regions: a scoping review.Frontiers in public health · 2023Article
- Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment.Current psychiatry reports · 2022Review
- Social media and schizophrenia: An update on clinical applications.World journal of psychiatry · 2022Review
- Intelligence Sensors and Sensing Spaces for Smart Home and Environment.Sensors (Basel, Switzerland) · 2022Article
- Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Schizophrenia is a severe mental disorder that ranks among the leading causes of disability worldwide. However, many cases of schizophrenia remain untreated due to failure to diagnose, self-denial, and social stigma. With the advent of social media, individuals suffering from schizophrenia share their mental health problems and seek support and treatment options. Machine learning approaches are increasingly used for detecting schizophrenia from social media posts. This study aims to determine whether machine learning could be effectively used to detect signs of schizophrenia in social media users by analyzing their social media texts. To this end, we collected posts from the social media platform Reddit focusing on schizophrenia, along with non-mental health related posts (fitness, jokes, meditation, parenting, relationships, and teaching) for the control group. We extracted linguistic features and content topics from the posts. Using supervised machine learning, we classified posts belonging to schizophrenia and interpreted important features to identify linguistic markers of schizophrenia. We applied unsupervised clustering to the features to uncover a coherent semantic representation of words in schizophrenia. We identified significant differences in linguistic features and topics including increased use of third person plural pronouns and negative emotion words and symptom-related topics. We distinguished schizophrenic from control posts with an accuracy of 96%. Finally, we found that coherent semantic groups of words were the key to detecting schizophrenia. Our findings suggest that machine learning approaches could help us understand the linguistic characteristics of schizophrenia and identify schizophrenia or otherwise at-risk individuals using social media texts.
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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.