ArticleInternational journal of environmental research and public health2022
Detecting and Analyzing Suicidal Ideation on Social Media Using Deep Learning and Machine Learning Models.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Diagnostic accuracy of machine learning approaches for suicide‑related outcomes: a meta‑analysis.Annals of general psychiatry · 2026Article
- Enhancing Korean adolescent suicide risk prediction with TabR: A generative AI and explainable retrieval-based deep learning approach.Medicine · 2026Article
- When a chatbot asks "How are you?": A cross-sectional study of AI call conversations and depressive symptom detection among older adults in rural South Korea.BMC geriatrics · 2026Article
- A comparative analysis of machine learning, deep learning, and ensemble learning models for suicidal ideation detection on social media.PLOS mental health · 2026Article
- Neuro-semantic graph fusion for explainable depression risk trajectory mapping using a graph-enhanced RoBERTa framework.Frontiers in artificial intelligence · 2026Article
- Digital Detection of Suicidal Ideation: A Scoping Review to Inform Prevention and Psychological Well-Being.Behavioral sciences (Basel, Switzerland) · 2025Review
- Multi-modal deep-attention-BiLSTM based early detection of mental health issues using social media posts.Scientific reports · 2025Article
- Psychosocial dynamics of suicidality and nonsuicidal self-injury: a digital linguistic perspective.Npj mental health research · 2025Article
- Risk-based evaluation of machine learning-based classification methods used for medical devices.BMC medical informatics and decision making · 2025Article
- An Explainable Artificial Intelligence Text Classifier for Suicidality Prediction in Youth Crisis Text Line Users: Development and Validation Study.JMIR public health and surveillance · 2025Article
- Mining Suicidal Ideation in Chinese Social Media: A Dual-Channel Deep Learning Model with Information Gain Optimization.Entropy (Basel, Switzerland) · 2025Article
- Explainable AI-based suicidal and non-suicidal ideations detection from social media text with enhanced ensemble technique.Scientific reports · 2025Article
- Review
- Suicide Risk Assessment on Social Media with Semi-Supervised Learning.Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data · 2024Article
- Measuring Raven's Progressive Matrices Combining Eye-Tracking Technology and Machine Learning (ML) Models.Journal of Intelligence · 2024Article
- Use of Digitalisation and Machine Learning Techniques in Therapeutic Intervention at Early Ages: Supervised and Unsupervised Analysis.Children (Basel, Switzerland) · 2024Article
- Young Adults' Exposure to and Engagement With Tobacco-Related Social Media Content and Subsequent Tobacco Use.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2024Article
- Efficacy of ChatGPT in Cantonese Sentiment Analysis: Comparative Study.Journal of medical Internet research · 2024Article
- Physical frailty identification using machine learning to explore the 5-item FRAIL scale, Cardiovascular Health Study index, and Study of Osteoporotic Fractures index.Frontiers in public health · 2024Article
- Neural activity during inhibitory control predicts suicidal ideation with machine learning.NPP - digital psychiatry and neuroscience · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Individuals who suffer from suicidal ideation frequently express their views and ideas on social media. Thus, several studies found that people who are contemplating suicide can be identified by analyzing social media posts. However, finding and comprehending patterns of suicidal ideation represent a challenging task. Therefore, it is essential to develop a machine learning system for automated early detection of suicidal ideation or any abrupt changes in a user's behavior by analyzing his or her posts on social media. In this paper, we propose a methodology based on experimental research for building a suicidal ideation detection system using publicly available Reddit datasets, word-embedding approaches, such as TF-IDF and Word2Vec, for text representation, and hybrid deep learning and machine learning algorithms for classification. A convolutional neural network and Bidirectional long short-term memory (CNN-BiLSTM) model and the machine learning XGBoost model were used to classify social posts as suicidal or non-suicidal using textual and LIWC-22-based features by conducting two experiments. To assess the models' performance, we used the standard metrics of accuracy, precision, recall, and F1-scores. A comparison of the test results showed that when using textual features, the CNN-BiLSTM model outperformed the XGBoost model, achieving 95% suicidal ideation detection accuracy, compared with the latter's 91.5% accuracy. Conversely, when using LIWC features, XGBoost showed better performance than CNN-BiLSTM.
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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.