RETRACTED: Innovative Use of Self-Attention-Based Ensemble Deep Learning for Suicide Risk Detection in Social Media Posts
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-expr...
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MDPI AG
2024-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/2/893 |
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| author | Hoan-Suk Choi Jinhong Yang |
| author_facet | Hoan-Suk Choi Jinhong Yang |
| author_sort | Hoan-Suk Choi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2ae6f2b245914dcda7eb13b7df7c8a2b |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-2ae6f2b245914dcda7eb13b7df7c8a2b2025-08-20T01:51:25ZengMDPI AGApplied Sciences2076-34172024-01-0114289310.3390/app14020893RETRACTED: Innovative Use of Self-Attention-Based Ensemble Deep Learning for Suicide Risk Detection in Social Media PostsHoan-Suk Choi0Jinhong Yang1KAIST-Megazone Cloud Intelligent Cloud Computing Convergence Research Center, Daejeon 34141, Republic of KoreaDepartment of Medical IT, INJE University, Gimhae 50843, Republic of KoreaSuicidal 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.https://www.mdpi.com/2076-3417/14/2/893bidirectional TCNLSTMNLPself-attentionsuicidal ideation |
| spellingShingle | Hoan-Suk Choi Jinhong Yang RETRACTED: Innovative Use of Self-Attention-Based Ensemble Deep Learning for Suicide Risk Detection in Social Media Posts Applied Sciences bidirectional TCN LSTM NLP self-attention suicidal ideation |
| title | RETRACTED: Innovative Use of Self-Attention-Based Ensemble Deep Learning for Suicide Risk Detection in Social Media Posts |
| title_full | RETRACTED: Innovative Use of Self-Attention-Based Ensemble Deep Learning for Suicide Risk Detection in Social Media Posts |
| title_fullStr | RETRACTED: Innovative Use of Self-Attention-Based Ensemble Deep Learning for Suicide Risk Detection in Social Media Posts |
| title_full_unstemmed | RETRACTED: Innovative Use of Self-Attention-Based Ensemble Deep Learning for Suicide Risk Detection in Social Media Posts |
| title_short | RETRACTED: Innovative Use of Self-Attention-Based Ensemble Deep Learning for Suicide Risk Detection in Social Media Posts |
| title_sort | retracted innovative use of self attention based ensemble deep learning for suicide risk detection in social media posts |
| topic | bidirectional TCN LSTM NLP self-attention suicidal ideation |
| url | https://www.mdpi.com/2076-3417/14/2/893 |
| work_keys_str_mv | AT hoansukchoi retractedinnovativeuseofselfattentionbasedensembledeeplearningforsuicideriskdetectioninsocialmediaposts AT jinhongyang retractedinnovativeuseofselfattentionbasedensembledeeplearningforsuicideriskdetectioninsocialmediaposts |