Deep Hierarchical Ensemble Model for Suicide Detection on Imbalanced Social Media Data
As a serious worldwide problem, suicide often causes huge and irreversible losses to families and society. Therefore, it is necessary to detect and help individuals with suicidal ideation in time. In recent years, the prosperous development of social media has provided new perspectives on suicide de...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/24/4/442 |
| _version_ | 1827628911611084800 |
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| author | Zepeng Li Jiawei Zhou Zhengyi An Wenchuan Cheng Bin Hu |
| author_facet | Zepeng Li Jiawei Zhou Zhengyi An Wenchuan Cheng Bin Hu |
| author_sort | Zepeng Li |
| collection | DOAJ |
| description | As a serious worldwide problem, suicide often causes huge and irreversible losses to families and society. Therefore, it is necessary to detect and help individuals with suicidal ideation in time. In recent years, the prosperous development of social media has provided new perspectives on suicide detection, but related research still faces some difficulties, such as data imbalance and expression implicitness. In this paper, we propose a Deep Hierarchical Ensemble model for Suicide Detection (DHE-SD) based on a hierarchical ensemble strategy, and construct a dataset based on Sina Weibo, which contains more than 550 thousand posts from 4521 users. To verify the effectiveness of the model, we also conduct experiments on a public Weibo dataset containing 7329 users’ posts. The proposed model achieves the best performance on both the constructed dataset and the public dataset. In addition, in order to make the model applicable to a wider population, we use the proposed sentence-level mask mechanism to delete user posts with strong suicidal ideation. Experiments show that the proposed model can still effectively identify social media users with suicidal ideation even when the performance of the baseline models decrease significantly. |
| first_indexed | 2024-03-09T13:42:19Z |
| format | Article |
| id | doaj.art-714bf628e60a48a28b93540f2f3a52de |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T13:42:19Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-714bf628e60a48a28b93540f2f3a52de2023-11-30T21:04:53ZengMDPI AGEntropy1099-43002022-03-0124444210.3390/e24040442Deep Hierarchical Ensemble Model for Suicide Detection on Imbalanced Social Media DataZepeng Li0Jiawei Zhou1Zhengyi An2Wenchuan Cheng3Bin Hu4School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou 730000, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou 730000, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou 730000, ChinaSchool of Information Science and Engineering, Lanzhou University, Lanzhou 730000, ChinaAs a serious worldwide problem, suicide often causes huge and irreversible losses to families and society. Therefore, it is necessary to detect and help individuals with suicidal ideation in time. In recent years, the prosperous development of social media has provided new perspectives on suicide detection, but related research still faces some difficulties, such as data imbalance and expression implicitness. In this paper, we propose a Deep Hierarchical Ensemble model for Suicide Detection (DHE-SD) based on a hierarchical ensemble strategy, and construct a dataset based on Sina Weibo, which contains more than 550 thousand posts from 4521 users. To verify the effectiveness of the model, we also conduct experiments on a public Weibo dataset containing 7329 users’ posts. The proposed model achieves the best performance on both the constructed dataset and the public dataset. In addition, in order to make the model applicable to a wider population, we use the proposed sentence-level mask mechanism to delete user posts with strong suicidal ideation. Experiments show that the proposed model can still effectively identify social media users with suicidal ideation even when the performance of the baseline models decrease significantly.https://www.mdpi.com/1099-4300/24/4/442social mediasuicide ideation detectiondeep neural networkimbalanced dataSina WeiboChina |
| spellingShingle | Zepeng Li Jiawei Zhou Zhengyi An Wenchuan Cheng Bin Hu Deep Hierarchical Ensemble Model for Suicide Detection on Imbalanced Social Media Data Entropy social media suicide ideation detection deep neural network imbalanced data Sina Weibo China |
| title | Deep Hierarchical Ensemble Model for Suicide Detection on Imbalanced Social Media Data |
| title_full | Deep Hierarchical Ensemble Model for Suicide Detection on Imbalanced Social Media Data |
| title_fullStr | Deep Hierarchical Ensemble Model for Suicide Detection on Imbalanced Social Media Data |
| title_full_unstemmed | Deep Hierarchical Ensemble Model for Suicide Detection on Imbalanced Social Media Data |
| title_short | Deep Hierarchical Ensemble Model for Suicide Detection on Imbalanced Social Media Data |
| title_sort | deep hierarchical ensemble model for suicide detection on imbalanced social media data |
| topic | social media suicide ideation detection deep neural network imbalanced data Sina Weibo China |
| url | https://www.mdpi.com/1099-4300/24/4/442 |
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