Suicidal Ideation Detection in Social Media using Optimized CNN-BiLSTM Architecture
DOI:
https://doi.org/10.30871/jaic.v10i1.11926Keywords:
CNN-BiLSTM, Deep Learning, Suicidal Ideation, Text Classification, Social MediaAbstract
This research aims to develop an optimized hybrid deep learning model for detecting suicidal ideation from social media text. The growing volume of online discussions, particularly on platforms such as Reddit, provides valuable signals for early identification of individuals at risk; however, the linguistic characteristics of user-generated content are highly diverse and often noisy. To address this challenge, this study proposes an Optimized CNN-BiLSTM architecture enhanced with a dropout rate of 0.6 and a strategic training approach utilizing Early Stopping (patience=3) and a Learning Rate Scheduler (ReduceLROnPlateau) to prevent local minima and ensure convergence stability. The dataset used consists of 232,074 text entries with a balanced class distribution (50% suicide, 50% non-suicide) to ensure the validity of evaluation metrics and eliminate majority class bias. Experimental results demonstrate that the optimized model achieves an accuracy of 94.96%, precision of 95.70%, recall of 94.15%, and an F1-score of 94.92%, indicating a significant improvement over the baseline CNN-BiLSTM and single BiLSTM models. Furthermore, interpretability analysis via keyword visualization (Word Cloud) validates that the model effectively captures semantically relevant emotional expressions of despair. These findings suggest that the optimized hybrid architecture provides a robust and operationally viable approach for supporting real-time early-warning systems on social media platforms to facilitate timely mental health interventions.
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[1] Goni, A., Jahangir, Md. U. F., Chowdhury, R. R., Akter, F., & Hussain, K. (2024). Detection of Suicidal Ideation on Social Media using Machine Learning Approaches. International Journal of Computer Applications, 186(51), 8–14. https://doi.org/10.5120/ijca2024924161.
[2] Aldhyani, T. H. H., Alsubari, S. N., Alshebami, A. S., Alkahtani, H., & Ahmed, Z. A. T. (2022). Detecting and Analyzing Suicidal Ideation on Social Media Using Deep Learning and Machine Learning Models. International Journal of Environmental Research and Public Health, 19(19), 12635. https://doi.org/10.3390/ijerph191912635.
[3] M. Chatterjee, P. Samanta, P. Kumar and D. Sarkar, "Suicide Ideation Detection using Multiple Feature Analysis from Twitter Data," 2022 IEEE Delhi Section Conference (DELCON), New Delhi, India, 2022, pp. 1-6, doi: 10.1109/DELCON54057.2022.9753295.
[4] Babulal, K. S., & Nayak, B. K. (2022). Suicidal Analysis on Social Networks Using Machine Learning (pp. 230–247). https://doi.org/10.4018/978-1-6684-3533-5.ch012
[5] Renjith, S., Abraham, A., Jyothi, S. B., Chandran, L., & Thomson, J. (2022). An ensemble deep learning technique for detecting suicidal ideation from posts in social media platforms. Journal of King Saud University - Computer and Information Sciences, 34(10), 9564–9575. https://doi.org/10.1016/j.jksuci.2021.11.010.
[6] Islam Bhuiyan, M., Pahang Al-Sultan Abdullah Pahang, M., Nur Shazwani Kamarudin, M., & Hafieza Ismail, N. (n.d.). Detecting Suicidal Ideation in Text with Interpretable Deep Learning: A CNN-BiGRU with Attention Mechanism.
[7] G. Ananthakrishnan, A. K. Jayaraman, T. E. Trueman, S. Mitra, A. A. K and A. Murugappan, "Suicidal Intention Detection in Tweets Using BERT-Based Transformers," 2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India, 2022, pp. 322-327, doi: 10.1109/ICCCIS56430.2022.10037677.
[8] Islam Bhuiyan, M., Shazwani Kamarudin, N., Hafieza Ismail, N., & Tasnim Raya, J. (2025). Enhanced Suicidal Ideation Detection from Social Media using a CNN-BiLSTM Hybrid Model. Semarak International Journal of Applied Psychology Journal Homepage, 8, 31–45. https://doi.org/10.37934/sijap.8.1.3145.
[9] Dzakwan Diash, H., Nathania, V., & Idhom, M. (2025). Application of CNN-BiLSTM Algorithm for Ethereum Price Prediction. In Journal of Applied Informatics and Computing (JAIC) (Vol. 9, Issue 4). http://jurnal.polibatam.ac.id/index.php/JAIC.
[10] Levkovich, I., & Omar, M. (2024). Evaluating of BERT-based and Large Language Mod for Suicide Detection, Prevention, and Risk Assessment: A Systematic Review. Journal of Medical Systems, 48(1), 113. https://doi.org/10.1007/s10916-024-02134-3.
[11] Yang, Z., Leonard, R., Tran, H., Driscoll, R., & Davis, C. (2025). Detection of Suicidal Risk on Social Media: A Hybrid Model. http://arxiv.org/abs/2505.23797.
[12] Vipin Kumar Sahu, & Dr. Brijendra Gupta. (2025). Prediction of Suicidal Ideation Using Deep Learning Models. International Journal of Advanced Research in Science, Communication and Technology, 228–235. https://doi.org/10.48175/IJARSCT-29632.
[13] Sanders, A., Strzalkowski, T., Si, M., Chang, A., Dey, D., Braasch, J., & Wang, D. (2022). Towards a Progression-Aware Autonomous Dialogue Agent. http://arxiv.org/abs/2205.03692.
[14] Bunnell, B. E., Tsalatsanis, A., Chaphalkar, C., Robinson, S., Klein, S., Cool, S., Szwast, E., Heider, P. M., Wolf, B. J., & Obeid, J. S. (2025). Automated detection and prediction of suicidal behavior from clinical notes using deep learning. PLOS One, 20(9), e0331459. https://doi.org/10.1371/journal.pone.0331459.
[15] N. Sharma and P. Karwasra, "Suicidal Text Detection on Social Media for Suicide Prevention Using Deep Learning Models," TENCON 2023 - 2023 IEEE Region 10 Conference (TENCON), Chiang Mai, Yin, J., Hou, B., Dai, J., & Zu, Y. (2024). A CNN-BiLSTM Method Based on Attention Mechanism for Class-imbalanced Abnormal Traffic Detection. Proceedings of the International Conference on Computer Vision and Deep Learning, 1–6. https://doi.org/10.1145/3653781.3653807
[16] Thailand, 2023, pp. 25-30, doi: 10.1109/TENCON58879.2023.10322499.
[17] Hu, Y.-H., Wu, R.-Y., Su, M.-Y., Lin, I.-L., & Shen, C.-C. (2025). Multimodal Multitask Learning for Predicting Depression Severity and Suicide Risk Using Pretrained Audio and Text Embeddings: Methodology Development and Application. JMIR Medical Informatics, 13, e66907–e66907. https://doi.org/10.2196/66907.
[18] Zevallos, R., Schoene, A., & Ortega, J. E. (2024). The First Multilingual Model For The Detection of Suicide Texts. http://arxiv.org/abs/2412.15498.
[19] Hansani, P., & Arachchi, K. (n.d.). Mental Health Prediction Using Social Media Posts: A Nature-Inspired Approach. https://doi.org/10.13140/RG.2.2.20212.21123.
[20] Hansani, P., & Arachchi, K. (n.d.). Mental Health Prediction Using Social Media Posts: A Nature-Inspired Approach. https://doi.org/10.13140/RG.2.2.20212.21123.
[21] Holmes, G., Tang, B., Gupta, S., Venkatesh, S., Christensen, H., & Whitton, A. (2025). Applications of Large Language Models in the Field of Suicide Prevention: Scoping Review. Journal of Medical Internet Research, 27, e63126. https://doi.org/10.2196/63126
[22] Amiriparian, S., Gerczuk, M., Lutz, J., Strube, W., Papazova, I., Hasan, A., Kathan, A., & Schuller, B. W. (2024). Non-Invasive Suicide Risk Prediction Through Speech Analysis. http://arxiv.org/abs/2404.12132.
[23] Cui, Z., Lei, C., Wu, W., Duan, Y., Qu, D., Wu, J., Chen, R., & Zhang, C. (2024). Spontaneous Speech-Based Suicide Risk Detection Using Whisper and Large Language Models. https://doi.org/10.21437/Interspeech.2024-1895.
[24] Grimland, M., Benatov, J., Yeshayahu, H., Izmaylov, D., Segal, A., Gal, K., & Levi‐Belz, Y. (2024). Predicting suicide risk in real‐time crisis hotline chats integrating machine learning with psychological factors: Exploring the black box. Suicide and Life-Threatening Behavior, 54(3), 416–424. https://doi.org/10.1111/sltb.13056.
[25] Chen, Y., Li, J., Song, C., Zhao, Q., Tong, Y., & Fu, G. (2024). Deep Learning and Large Language Models for Audio and Text Analysis in Predicting Suicidal Acts in Chinese Psychological Support Hotlines. http://arxiv.org/abs/2409.06164.
[26] Iyer, R., Nedeljkovic, M., & Meyer, D. (2022). Using Voice Biomarkers to Classify Suicide Risk in Adult Telehealth Callers: Retrospective Observational Study. JMIR Mental Health, 9(8), e39807. https://doi.org/10.2196/39807.
[27] Thakur, R. S., & Singh, J. (2025). AI and NLP for Mental Health Prediction from Social Media: A Decade of Progress, Challenges, and Explainability (2015–2025). International Journal for Research in Applied Science and Engineering Technology, 13(8), 1707–1715. https://doi.org/10.22214/ijraset.2025.73857.
[28] Chatterjee, M., Kumar, P., Samanta, P., & Sarkar, D. (2022). Suicide ideation detection from online social media: A multi-modal feature based technique. International Journal of Information Management Data Insights, 2(2), 100103. https://doi.org/10.1016/j.jjimei.2022.100103.
[29] Jin, J., Liu, Y., & Dai, Y. (2025). Multimodal Speech and Text Models to Detect Suicidal Risks in Adolescents. https://doi.org/10.1101/2025.07.31.25332367.
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