# Detecting Suicidal Ideation in Chinese Microblogs with Psychological Lexicons

arXiv: https://arxiv.org/abs/1411.0778 · HTML: https://mlsanity.com/paper/1411.0778/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2014.

## Built on by (most cited first, 6 of 6)

- [Suicidal Ideation Detection on Social Media: A Review of Machine Learning Methods](https://mlsanity.com/paper/2201.10515/) (2022)
- [Predicting mental health using social media: A roadmap for future development](https://mlsanity.com/paper/2301.10453/) (2023)
- [Detecting Suicidality in Arabic Tweets Using Machine Learning and Deep Learning Techniques](https://mlsanity.com/paper/2309.00246/) (2023)
- [Lexicography Saves Lives (LSL): Automatically Translating Suicide-Related Language](https://mlsanity.com/paper/2412.15497/) (2024)
- [CapsF: Capsule Fusion for Extracting psychiatric stressors for suicide from twitter](https://mlsanity.com/paper/2403.15391/) (2024)
- [Reliable Self-Harm Risk Screening via Adaptive Multi-Agent LLM Systems](https://mlsanity.com/paper/2604.22154/) (2026)

## Abstract (arXiv)

Suicide is among the leading causes of death in China. However, technical approaches toward preventing suicide are challenging and remaining under development. Recently, several actual suicidal cases were preceded by users who posted microblogs with suicidal ideation to Sina Weibo, a Chinese social media network akin to Twitter. It would therefore be desirable to detect suicidal ideations from microblogs in real-time, and immediately alert appropriate support groups, which may lead to successful prevention. In this paper, we propose a real-time suicidal ideation detection system deployed over Weibo, using machine learning and known psychological techniques. Currently, we have identified 53 known suicidal cases who posted suicide notes on Weibo prior to their deaths.We explore linguistic features of these known cases using a psychological lexicon dictionary, and train an effective suicidal Weibo post detection model. 6714 tagged posts and several classifiers are used to verify the model. By combining both machine learning and psychological knowledge, SVM classifier has the best performance of different classifiers, yielding an F-measure of 68:3%, a Precision of 78:9%, and a Recall of 60:3%.