# Suicide Risk Assessment on Social Media with Semi-Supervised Learning

arXiv: https://arxiv.org/abs/2411.12767 · HTML: https://mlsanity.com/paper/2411.12767/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 29 references that are in the map.
- First posted on arXiv 18 Nov 2024.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [The case for 4-bit precision: k-bit Inference Scaling Laws](https://mlsanity.com/paper/2212.09720/) (2022)
- [Metrics for Multi-Class Classification: an Overview](https://mlsanity.com/paper/2008.05756/) (2020)
- [Open, Closed, or Small Language Models for Text Classification?](https://mlsanity.com/paper/2308.10092/) (2023)
- [Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications](https://mlsanity.com/paper/1910.12611/) (2019)

## Built on by (most cited first, 1 of 1)

- [Beyond Risk Prediction: Evidence Grounding and Psychosocial Factor Verification for Explainable Suicide Risk Assessment](https://mlsanity.com/paper/2610.08842/) (2026)

## Abstract (arXiv)

With social media communities increasingly becoming places where suicidal individuals post and congregate, natural language processing presents an exciting avenue for the development of automated suicide risk assessment systems. However, past efforts suffer from a lack of labeled data and class imbalances within the available labeled data. To accommodate this task's imperfect data landscape, we propose a semi-supervised framework that leverages labeled (n=500) and unlabeled (n=1,500) data and expands upon the self-training algorithm with a novel pseudo-label acquisition process designed to handle imbalanced datasets. To further ensure pseudo-label quality, we manually verify a subset of the pseudo-labeled data that was not predicted unanimously across multiple trials of pseudo-label generation. We test various models to serve as the backbone for this framework, ultimately deciding that RoBERTa performs the best. Ultimately, by leveraging partially validated pseudo-labeled data in addition to ground-truth labeled data, we substantially improve our model's ability to assess suicide risk from social media posts.