Weakly-Supervised Methods for Suicide Risk Assessment: Role of Related Domains (2021.acl-short)
Copied to clipboard
| 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) |
Similar Papers
A Prioritization Model for Suicidality Risk Assessment (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for predicting suicide have failed for fifty years . however, with the advent of machine learning, the problem is gaining momentum . |
| Approach: | They propose a method that jointly ranks individuals and their social media posts to improve suicide risk assessment. |
| Outcome: | The proposed approach outperforms existing methods in a case study using expert-annotated test collection. |
A Risk-Averse Mechanism for Suicidality Assessment on Social Media (2022.acl-short)
Copied to clipboard
| 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. |
Do Models of Mental Health Based on Social Media Data Generalize? (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing literature on the validity of proxy-based methods for annotating mental health status in social media has raised new concerns regarding their use in clinical applications. |
| Approach: | They explore the generalization ability of machine learning classifiers trained to detect depression in individuals across multiple social media platforms. |
| Outcome: | The proposed methods show that they can be used to train and analyze large datasets and that they are robust to large dataset sizes. |
Leveraging Mental Health Forums for User-level Depression Detection on Social Media (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing methods to detect depression on social media platforms are limited due to the vastness of social media content and the lack of linguistic features. |
| Approach: | They propose to optimize the performance of user-level depression classification to lessen the burden on computational resources. |
| Outcome: | The proposed system outperforms baselines across standard metrics for the task of depression detection in text. |
MentalHelp: A Multi-Task Dataset for Mental Health in Social Media (2024.lrec-main)
Copied to clipboard
Nishat Raihan, Sadiya Sayara Chowdhury Puspo, Shafkat Farabi, Ana-Maria Bucur, Tharindu Ranasinghe, Marcos Zampieri
| Challenge: | Annotating social media data for mental health disorders is expensive and time-consuming, limiting their size and scope. |
| Approach: | They present a large-scale semi-supervised mental disorder detection dataset containing 14 million instances from Reddit and an ensemble of three separate models. |
| Outcome: | The proposed dataset contains 14 million instances of mental disorders . it was collected from reddit and labeled in a semi-supervised way . |
Detecting Suicide Risk in Online Counseling Services: A Study in a Low-Resource Language (2022.coling-1)
Copied to clipboard
| 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. |
A Computational Approach to Feature Extraction for Identification of Suicidal Ideation in Tweets (P18-3)
Copied to clipboard
| Challenge: | Suicidal ideation on social media websites is associated with higher suicide rates . suicide is the second leading cause of death among 15-29-year-olds . |
| Approach: | They propose a supervised method for detecting suicidal ideation in tweets using a dataset of manually annotated tweets. |
| Outcome: | The proposed method is compared against four baselines to validate its utility. |
Suicidal Risk Detection for Military Personnel (2020.emnlp-main)
Copied to clipboard
| Challenge: | a dataset of 2,791 posts with 13,955 expert annotations of suicidal risk levels is available for research . Suicide is one of the major causes of death in the military. |
| Approach: | They analyze posts related to military service in the Republic of Korea and annotate them with military experts and mental health experts. |
| Outcome: | The proposed method predicts the level of suicide risk, reaching .88 F1 for classifying the risks. |
Can Large Language Models Identify Implicit Suicidal Ideation? An Empirical Evaluation (2025.findings-emnlp)
Copied to clipboard
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 . |
| Approach: | They propose a dataset of 1,200 test cases simulating implicit suicidal ideation in private contexts. |
| Outcome: | The proposed dataset includes 1,200 test cases simulating implicit suicidal ideation in dialogue scenarios. |
Classifying Social Media Users before and after Depression Diagnosis via Their Language Usage: A Dataset and Study (2024.lrec-main)
Copied to clipboard
| Challenge: | Mental illness can negatively impact individuals’ quality of life as it is considered one of the causes of years lived with disability and it is related to high suicide rates. |
| Approach: | They collect first dataset of textual posts by same users before and after being diagnosed with depression and build multiple predictive models based on Transformers and BERT. |
| Outcome: | The proposed model can be used to detect depression and suicidal thoughts in users who are not diagnosed with depression or suicide. |