Detecting Suicide Risk in Online Counseling Services: A Study in a Low-Resource Language (2022.coling-1)
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| Challenge: | Existing domain-specific models for detecting suicide are lacking in low-resource languages. |
| Approach: | They propose a model that combines pre-trained language models with a fixed set of suicidal cues and a two-stage fine-tuning process to detect SI. |
| Outcome: | The proposed model outperforms baseline models even early on in the conversation and performs well across genders and age groups. |
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| Challenge: | Existing models for suicide prevention are limited in domains and are not available in low-resource languages. |
| Approach: | They propose a computational model that combines pre-trained language models with a fixed set of manually crafted suicidal cues and a two-stage fine-tuning process to detect suicide risk. |
| Outcome: | The proposed model outperforms baseline models even early on in the conversation and performs well across genders and age groups. |
PsyGUARD: An Automated System for Suicide Detection and Risk Assessment in Psychological Counseling (2024.emnlp-main)
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| Challenge: | Existing systems for fine-grained suicide detection and risk assessment are lacking . a lack of domain-specific systems for this task poses a challenge to automated crisis intervention aimed at suicide prevention. |
| Approach: | They propose to use a fine-grained suicide detection system to assess risk in counseling . they develop a taxonomy for detecting suicide ideation and a large-scale dataset . |
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Can Large Language Models Identify Implicit Suicidal Ideation? An Empirical Evaluation (2025.findings-emnlp)
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Tong Li, Shu Yang, Junchao Wu, Jiyao Wei, Lijie Hu, Mengdi Li, Derek F. Wong, Joshua R. Oltmanns, Di Wang
| Challenge: | Existing data on suicidal ideation in private conversations are limited . a new dataset of 1,200 test cases is presented to address this gap . |
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Uncovering Intervention Opportunities for Suicide Prevention with Language Model Assistants (2026.acl-long)
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Jaspreet Ranjit, Hyundong Justin Cho, Claire J. Smerdon, Yoonsoo Nam, Myles Phung, Jonathan May, John R. Blosnich, Swabha Swayamdipta
| Challenge: | Using language models, annotators can help develop novel suicide interventions . 85% of cases where LM predictions disagree with existing annotations are analyzed . |
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Weakly-Supervised Methods for Suicide Risk Assessment: Role of Related Domains (2021.acl-short)
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| Challenge: | Among social media platforms, Reddit has emerged as the most promising one due to its anonymity and its focus on topic-based communities (subreddits) . a challenge for previous work on suicide risk assessment has been the small amount of labeled data. |
| Approach: | They propose to use social media to collect user data from r/SuicideWatch subreddit and annotate it with user-level suicide risk: no-risk, low-risk and high-risk. |
| Outcome: | The proposed model improves by using pseudo-labeling based on related issues around mental health (e.g., anxiety, depression) |
Towards Intention Understanding in Suicidal Risk Assessment with Natural Language Processing (2022.findings-emnlp)
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| Challenge: | Suicide is a global problem, with one suicide case for every 100 deaths worldwide . social networking sites are an essential forum for communication and information sharing . |
| Approach: | This paper compares natural language processing to suicidal ideation detection and risk assessment . it urges better intention understanding for reliable suicide risk assessment with computational methods . |
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RISE: Robust Early-exiting Internal Classifiers for Suicide Risk Evaluation (2024.lrec-main)
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| Challenge: | Existing systems for risk assessment are prone to incorrectly predicting risk severity and have no early detection mechanisms. |
| Approach: | They propose a novel mechanism for accurate early detection of suicide risk by ensembling Hyperbolic Internal Classifiers equipped with an abstention mechanism and early exit inference capabilities. |
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A Risk-Averse Mechanism for Suicidality Assessment on Social Media (2022.acl-short)
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| Challenge: | Social media has become a platform for users to express suicidal thoughts outside traditional clinical settings. |
| Approach: | They propose a risk-averse hierarchical attention classifier that refrains from making uncertain predictions on real-world Reddit data. |
| Outcome: | The proposed system can refrain from 83% of incorrect predictions on real-world Reddit data. |
Cross-Lingual Suicidal-Oriented Word Embedding toward Suicide Prevention (2020.findings-emnlp)
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| Challenge: | Existing suicide dictionaries for other languages have been limited to Korean . a model that uses social media data to identify whether a post includes suicidal ideation is useful . |
| Approach: | They propose a model that uses existing suicide dictionaries for Korean to predict suicidal ideation . they use the existing dictionary for English and Chinese to translate a post into English and then use the separate suicide-oriented embeddings for English. |
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Event Detection for Suicide Understanding (2022.findings-naacl)
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| Challenge: | Existing methods for detecting suicide-related events are limited . recognizing suicide- related events is critical to understanding the condition, authors argue . |
| Approach: | They propose a dataset to detect event trigger words of suicide-related events in forums . they propose 'suicideED' dataset to capture suicidal actions and ideation . |
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