RETRACTED: Innovative Use of Self-Attention-Based Ensemble Deep Learning for Suicide Risk Detection in Social Media Posts
This record is marked as retracted by the source. Review the publisher’s notice before using its findings.
Source abstract
Abstract
Suicidal ideation constitutes a critical concern in mental health, adversely affecting individuals and society at large. The early detection of such ideation is vital for providing timely support to individuals and mitigating its societal impact. With social media serving as a platform for self-expression, it offers a rich source of data that can reveal early symptoms of mental health issues. This paper introduces an innovative ensemble learning method named LSTM-Attention-BiTCN, which fuses LSTM and BiTCN models with a self-attention mechanism to detect signs of suicidality in social media posts. Our LSTM-Attention-BiTCN model demonstrated superior performance in comparison to baseline models in the realm of classification and suicidal ideation detection, boasting an accuracy of 0.9405, a precision of 0.9385, a recall of 0.9424, and an F1-score of 0.9405. Our proposed model can aid healthcare professionals in recognizing suicidal tendencies among social media users accurately, thereby contributing to efforts to reduce suicide rates.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Mental Health via Writing · Suicide and Self-Harm Studies · Digital Mental Health Interventions
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
KAIST-Megazone Cloud Intelligent Cloud Computing Convergence Research Center, Daejeon 34141, Republic of Korea
Department of Medical IT, INJE University, Gimhae 50843, Republic of Korea
References (26)
Show source-provided references
1. (2023, August 30). A World Health Organization Suicide. Available online: https://www.who.int/news-room/fact-sheets/detail/suicide/.
2. (2023, August 30). American Foundation for Suicide Prevention Suicide Statistics. Available online: https://afsp.org/suicide-statistics/.
3. Balbuena, L.D., Baetz, M., Sexton, J.A., Harder, D., Feng, C.X., Boctor, K., LaPointe, C., Letwiniuk, E., Shamloo, A., and Ishwaran, H. (2022). Identifying Long-Term and Imminent Suicide Predictors in a General Population and a Clinical Sample with Machine Learning. BMC Psychiatry, 22.10.1186/s12888-022-03702-y ↗
4. An across Online Social Networks Profile Building Approach: Application to Suicidal Ideation Detection10.1016/j.future.2022.03.017 ↗
5. Short-Term Prediction of Suicidal Thoughts and Behaviors in Adolescents: Can Recent Developments in Technology and Computational Science Provide a Breakthrough?10.1016/j.jad.2019.03.044 ↗
6. Comparing Mental Health of Facebook Users and Facebook Non-Users in an Inpatient Sample in Germany10.1016/j.jad.2019.08.078 ↗
7. Can Social Media Help Mental Health Practitioners Prevent Suicides?
8. Supervised Learning for Suicidal Ideation Detection in Online User Content10.1155/2018/6157249 ↗
9. Building and Using Personal Knowledge Graph to Improve Suicidal Ideation Detection on Social Media10.1109/TMM.2020.3046867 ↗
10. Depression and Suicide Risk Detection on Social Media Using fastText Embedding and XGBoost Classifier10.1016/j.procs.2023.01.141 ↗
11. Using Weak Supervision and Deep Learning to Classify Clinical Notes for Identification of Current Suicidal Ideation10.1016/j.jpsychires.2021.01.052 ↗
12. Fair and Explainable Depression Detection in Social Media10.1016/j.ipm.2022.103168 ↗
13. Tadesse, M.M., Lin, H., Xu, B., and Yang, L. (2020). Detection of Suicide Ideation in Social Media Forums Using Deep Learning. Algorithms, 13.10.3390/a13010007 ↗
14. Moschitti, A., Pang, B., and Daelemans, W. (2014, January 25–29). GloVe: Global Vectors for Word Representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar.
15. Brownlee, J. (2017). Long Short-Term Memory Networks With Python: Develop Sequence Prediction Models with Deep Learning, Machine Learning Mastery.
16. Bai, S., Zico Kolter, J., and Koltun, V. (2018). An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. arXiv.
17. Offensive Text Detection Using Temporal Convolutional Networks
18. Short Text Classification Based on Bidirectional TCN and Attention Mechanism10.1088/1742-6596/1693/1/012067 ↗
19. Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural Machine Translation by Jointly Learning to Align and Translate. arXiv.
20. Nikhileswar, K. (2023, August 28). Suicide and Depression Detection 2021. Available online: https://www.kaggle.com/datasets/nikhileswarkomati/suicide-watch.
21. K-Nearest Neighbor10.4249/scholarpedia.1883 ↗
22. Random Forests10.1023/A:1010933404324 ↗
23. Ayyadevara, V.K. (2018). Decision Tree. Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R, Apress.10.1007/978-1-4842-3564-5 ↗
24. Ayyadevara, V.K. (2018). Gradient Boosting Machine. Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R, Apress.10.1007/978-1-4842-3564-5 ↗
25. Chen, T., and Guestrin, C. (2016, January 13–17). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.10.1145/2939672.2939785 ↗
26. Bisong, E. (2019). Logistic Regression. Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners, Apress.10.1007/978-1-4842-4470-8 ↗