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    <abstract>Despite detection of suicidal ideation on social media has made great progress in recent years, people’s implicitly and anti-real contrarily expressed posts still remain as an obstacle, constraining the detectors to acquire higher satisfactory performance. Enlightened by the hidden “tree holes” phenomenon on microblog, where people at suicide risk tend to disclose their inner real feelings and thoughts to the microblog space whose authors have committed suicide, we explore the use of tree holes to enhance microblog-based suicide risk detection from the following two perspectives. (1) We build suicide-oriented word embeddings based on tree hole contents to strength the sensibility of suicide-related lexicons and context based on tree hole contents. (2) A two-layered attention mechanism is deployed to grasp intermittently changing points from individual’s open blog streams, revealing one’s inner emotional world more or less. Our experimental results show that with suicide-oriented word embeddings and attention, microblog-based suicide risk detection can achieve over 91% accuracy. A large-scale well-labelled suicide data set is also reported in the paper.</abstract>
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%0 Conference Proceedings
%T Latent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention
%A Cao, Lei
%A Zhang, Huijun
%A Feng, Ling
%A Wei, Zihan
%A Wang, Xin
%A Li, Ningyun
%A He, Xiaohao
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F cao-etal-2019-latent
%X Despite detection of suicidal ideation on social media has made great progress in recent years, people’s implicitly and anti-real contrarily expressed posts still remain as an obstacle, constraining the detectors to acquire higher satisfactory performance. Enlightened by the hidden “tree holes” phenomenon on microblog, where people at suicide risk tend to disclose their inner real feelings and thoughts to the microblog space whose authors have committed suicide, we explore the use of tree holes to enhance microblog-based suicide risk detection from the following two perspectives. (1) We build suicide-oriented word embeddings based on tree hole contents to strength the sensibility of suicide-related lexicons and context based on tree hole contents. (2) A two-layered attention mechanism is deployed to grasp intermittently changing points from individual’s open blog streams, revealing one’s inner emotional world more or less. Our experimental results show that with suicide-oriented word embeddings and attention, microblog-based suicide risk detection can achieve over 91% accuracy. A large-scale well-labelled suicide data set is also reported in the paper.
%R 10.18653/v1/D19-1181
%U https://aclanthology.org/D19-1181
%U https://doi.org/10.18653/v1/D19-1181
%P 1718-1728

Markdown (Informal)

[Latent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention](https://aclanthology.org/D19-1181) (Cao et al., EMNLP-IJCNLP 2019)

• Latent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention (Cao et al., EMNLP-IJCNLP 2019)

ACL

• Lei Cao, Huijun Zhang, Ling Feng, Zihan Wei, Xin Wang, Ningyun Li, and Xiaohao He. 2019. Latent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1718–1728, Hong Kong, China. Association for Computational Linguistics.