# A Quantitative and Qualitative Analysis of Suicide Ideation Detection using Deep Learning

arXiv: https://arxiv.org/abs/2206.08673 · HTML: https://mlsanity.com/paper/2206.08673/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 17 Jun 2022.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [A Joint Model for Word Embedding and Word Morphology](https://mlsanity.com/paper/1606.02601/) (2016)

## Built on by (most cited first, 2 of 2)

- [A Comparative Analysis of Transformer and LSTM Models for Detecting Suicidal Ideation on Reddit](https://mlsanity.com/paper/2411.15404/) (2024)
- [Evaluating Transformer Models for Suicide Risk Detection on Social Media](https://mlsanity.com/paper/2410.08375/) (2024)

## Abstract (arXiv)

For preventing youth suicide, social media platforms have received much attention from researchers. A few researches apply machine learning, or deep learning-based text classification approaches to classify social media posts containing suicidality risk. This paper replicated competitive social media-based suicidality detection/prediction models. We evaluated the feasibility of detecting suicidal ideation using multiple datasets and different state-of-the-art deep learning models, RNN-, CNN-, and Attention-based models. Using two suicidality evaluation datasets, we evaluated 28 combinations of 7 input embeddings with 4 commonly used deep learning models and 5 pretrained language models in quantitative and qualitative ways. Our replication study confirms that deep learning works well for social media-based suicidality detection in general, but it highly depends on the dataset's quality.