# Suicidal Ideation Detection on Social Media: A Review of Machine Learning Methods

arXiv: https://arxiv.org/abs/2201.10515 · HTML: https://mlsanity.com/paper/2201.10515/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 62 references that are in the map.
- First posted on arXiv 25 Jan 2022.

## Builds on (most cited first, 3 of 3)

- [Detecting Suicidal Ideation in Chinese Microblogs with Psychological Lexicons](https://mlsanity.com/paper/1411.0778/) (2014)
- [Evolution of Reddit: From the Front Page of the Internet to a Self-referential Community?](https://mlsanity.com/paper/1402.1386/) (2014)
- [You are your Metadata: Identification and Obfuscation of Social Media Users using Metadata Information](https://mlsanity.com/paper/1803.10133/) (2018)

## Built on by (most cited first, 4 of 4)

- [Socially Aware Synthetic Data Generation for Suicidal Ideation Detection Using Large Language Models](https://mlsanity.com/paper/2402.01712/) (2024)
- [ChatGPT for Suicide Risk Assessment on Social Media: Quantitative Evaluation of Model Performance, Potentials and Limitations](https://mlsanity.com/paper/2306.09390/) (2023)
- [Towards Safer Pretraining: Analyzing and Filtering Harmful Content in Webscale datasets for Responsible LLMs](https://mlsanity.com/paper/2505.02009/) (2025)
- [Two-Stage Voting for Robust and Efficient Suicide Risk Detection on Social Media](https://mlsanity.com/paper/2510.08365/) (2025)

## Abstract (arXiv)

Social media platforms have transformed traditional communication methods by allowing users worldwide to communicate instantly, openly, and frequently. People use social media to express their opinion and share their personal stories and struggles. Negative feelings that express hardship, thoughts of death, and self-harm are widespread in social media, especially among young generations. Therefore, using social media to detect and identify suicidal ideation will help provide proper intervention that will eventually dissuade others from self-harming and committing suicide and prevent the spread of suicidal ideations on social media. Many studies have been carried out to identify suicidal ideation and behaviors in social media. This paper presents a comprehensive summary of current research efforts to detect suicidal ideation using machine learning algorithms on social media. This review 24 studies investigating the feasibility of social media usage for suicidal ideation detection is intended to facilitate further research in the field and will be a beneficial resource for researchers engaged in suicidal text classification.