# Learning Models for Suicide Prediction from Social Media Posts

arXiv: https://arxiv.org/abs/2105.03315 · HTML: https://mlsanity.com/paper/2105.03315/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 45 references that are in the map.
- First posted on arXiv 29 Apr 2021.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Word Affect Intensities](https://mlsanity.com/paper/1704.08798/) (2017)
- [An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation](https://mlsanity.com/paper/1607.05368/) (2016)
- [Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications](https://mlsanity.com/paper/1910.12611/) (2019)
- [Latent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention](https://mlsanity.com/paper/1910.12038/) (2019)
- [Building and Using Personal Knowledge Graph to Improve Suicidal Ideation Detection on Social Media](https://mlsanity.com/paper/2012.09123/) (2020)
- [Using Linguistic Features to Estimate Suicide Probability of Chinese Microblog Users](https://mlsanity.com/paper/1411.0861/) (2014)
- [Explainable CNN-attention Networks (C-Attention Network) for Automated Detection of Alzheimer's Disease](https://mlsanity.com/paper/2006.14135/) (2020)

## Built on by (most cited first, 10 of 10)

- [Explainable AI for Mental Disorder Detection via Social Media: A survey and outlook](https://mlsanity.com/paper/2406.05984/) (2024)
- [Does Rationale Quality Matter? Enhancing Mental Disorder Detection via Selective Reasoning Distillation](https://mlsanity.com/paper/2505.20014/) (2025)
- [A Simple and Flexible Modeling for Mental Disorder Detection by Learning from Clinical Questionnaires](https://mlsanity.com/paper/2306.02955/) (2023)
- [Quantifying the Suicidal Tendency on Social Media: A Survey](https://mlsanity.com/paper/2110.03663/) (2021)
- [Deep Knowledge-Infusion For Explainable Depression Detection](https://mlsanity.com/paper/2409.02122/) (2024)
- [A Big Data Analytics System for Predicting Suicidal Ideation in Real-Time Based on Social Media Streaming Data](https://mlsanity.com/paper/2404.12394/) (2024)
- [Understanding Mental Health Content on Social Media and Its Effect Towards Suicidal Ideation](https://mlsanity.com/paper/2501.09309/) (2025)
- [Analysis and Evaluation of Explainable Artificial Intelligence on Suicide Risk Assessment](https://mlsanity.com/paper/2303.06052/) (2023)
- [Risk prediction of pathological gambling on social media](https://mlsanity.com/paper/2403.19358/) (2024)
- [Reading Between the Signs: Predicting Future Suicidal Ideation from Adolescent Social Media Texts](https://mlsanity.com/paper/2509.03530/) (2025)

## Abstract (arXiv)

We propose a deep learning architecture and test three other machine learning models to automatically detect individuals that will attempt suicide within (1) 30 days and (2) six months, using their social media post data provided in the CLPsych 2021 shared task. Additionally, we create and extract three sets of handcrafted features for suicide risk detection based on the three-stage theory of suicide and prior work on emotions and the use of pronouns among persons exhibiting suicidal ideations. Extensive experimentations show that some of the traditional machine learning methods outperform the baseline with an F1 score of 0.741 and F2 score of 0.833 on subtask 1 (prediction of a suicide attempt 30 days prior). However, the proposed deep learning method outperforms the baseline with F1 score of 0.737 and F2 score of 0.843 on subtask 2 (prediction of suicide 6 months prior).