# Depression and Self-Harm Risk Assessment in Online Forums

arXiv: https://arxiv.org/abs/1709.01848 · HTML: https://mlsanity.com/paper/1709.01848/

## Key facts

- Cited by 84 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Sep 2017.

## Built on by (most cited first, 30 of 84)

- [PsyQA: A Chinese Dataset for Generating Long Counseling Text for Mental Health Support](https://mlsanity.com/paper/2106.01702/) (2021)
- [Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling](https://mlsanity.com/paper/2107.03451/) (2021)
- [Reducing Privacy Risks in Online Self-Disclosures with Language Models](https://mlsanity.com/paper/2311.09538/) (2023)
- [CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts](https://mlsanity.com/paper/2207.04674/) (2022)
- [Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires](https://mlsanity.com/paper/2204.10432/) (2022)
- [Symptom Identification for Interpretable Detection of Multiple Mental Disorders](https://mlsanity.com/paper/2205.11308/) (2022)
- [It's Just a Matter of Time: Detecting Depression with Time-Enriched Multimodal Transformers](https://mlsanity.com/paper/2301.05453/) (2023)
- [Identifying Moments of Change from Longitudinal User Text](https://mlsanity.com/paper/2205.05593/) (2022)
- [Early Risk Detection of Pathological Gambling, Self-Harm and Depression Using BERT](https://mlsanity.com/paper/2106.16175/) (2021)
- [Read, Diagnose and Chat: Towards Explainable and Interactive LLMs-Augmented Depression Detection in Social Media](https://mlsanity.com/paper/2305.05138/) (2023)
- [An Exploratory Analysis of the Relation Between Offensive Language and Mental Health](https://mlsanity.com/paper/2105.14888/) (2021)
- [Micromodels for Efficient, Explainable, and Reusable Systems: A Case Study on Mental Health](https://mlsanity.com/paper/2109.13770/) (2021)
- [Emotion fusion for mental illness detection from social media: A survey](https://mlsanity.com/paper/2304.09493/) (2023)
- [Still Not Quite There! Evaluating Large Language Models for Comorbid Mental Health Diagnosis](https://mlsanity.com/paper/2410.03908/) (2024)
- [Psychiatric Scale Guided Risky Post Screening for Early Detection of Depression](https://mlsanity.com/paper/2205.09497/) (2022)
- [MentSum: A Resource for Exploring Summarization of Mental Health Online Posts](https://mlsanity.com/paper/2206.00856/) (2022)
- [Reading Between the Frames: Multi-Modal Depression Detection in Videos from Non-Verbal Cues](https://mlsanity.com/paper/2401.02746/) (2024)
- [DepressionNet: A Novel Summarization Boosted Deep Framework for Depression Detection on Social Media](https://mlsanity.com/paper/2105.10878/) (2021)
- [FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning](https://mlsanity.com/paper/2310.16538/) (2023)
- [Explainable Depression Symptom Detection in Social Media](https://mlsanity.com/paper/2310.13664/) (2023)
- [Combining Hierachical VAEs with LLMs for clinically meaningful timeline summarisation in social media](https://mlsanity.com/paper/2401.16240/) (2024)
- [Hope Speech detection in under-resourced Kannada language](https://mlsanity.com/paper/2108.04616/) (2021)
- [Sequence-to-Sequence Lexical Normalization with Multilingual Transformers](https://mlsanity.com/paper/2110.02869/) (2021)
- [Prediction of Depression Severity Based on the Prosodic and Semantic Features with Bidirectional LSTM and Time Distributed CNN](https://mlsanity.com/paper/2202.12456/) (2022)
- [Semantic Similarity Models for Depression Severity Estimation](https://mlsanity.com/paper/2211.07624/) (2022)
- [Exploring Social Media for Early Detection of Depression in COVID-19 Patients](https://mlsanity.com/paper/2302.12044/) (2023)
- [Explainable AI for Mental Disorder Detection via Social Media: A survey and outlook](https://mlsanity.com/paper/2406.05984/) (2024)
- [On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook](https://mlsanity.com/paper/2410.08793/) (2024)
- [Survey and Experiments on Mental Disorder Detection via Social Media: From Large Language Models and RAG to Agents](https://mlsanity.com/paper/2504.02800/) (2025)
- [Detecting Reddit Users with Depression Using a Hybrid Neural Network SBERT-CNN](https://mlsanity.com/paper/2302.02759/) (2023)

## Abstract (arXiv)

Users suffering from mental health conditions often turn to online resources for support, including specialized online support communities or general communities such as Twitter and Reddit. In this work, we present a neural framework for supporting and studying users in both types of communities. We propose methods for identifying posts in support communities that may indicate a risk of self-harm, and demonstrate that our approach outperforms strong previously proposed methods for identifying such posts. Self-harm is closely related to depression, which makes identifying depressed users on general forums a crucial related task. We introduce a large-scale general forum dataset ("RSDD") consisting of users with self-reported depression diagnoses matched with control users. We show how our method can be applied to effectively identify depressed users from their use of language alone. We demonstrate that our method outperforms strong baselines on this general forum dataset.