# A Comparative Analysis of Transformer and LSTM Models for Detecting Suicidal Ideation on Reddit

arXiv: https://arxiv.org/abs/2411.15404 · HTML: https://mlsanity.com/paper/2411.15404/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 41 references that are in the map.
- First posted on arXiv 23 Nov 2024.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey](https://mlsanity.com/paper/1711.04305/) (2017)
- [A Survey on Text Classification: From Shallow to Deep Learning](https://mlsanity.com/paper/2008.00364/) (2020)
- [Comparing BERT against traditional machine learning text classification](https://mlsanity.com/paper/2005.13012/) (2020)
- [Comparative Analysis of Word Embeddings for Capturing Word Similarities](https://mlsanity.com/paper/2005.03812/) (2020)
- [A Survey-Based Qualitative Study to Characterize Expectations of Software Developers from Five Stakeholders](https://mlsanity.com/paper/2107.09587/) (2021)
- [A Quantitative and Qualitative Analysis of Suicide Ideation Detection using Deep Learning](https://mlsanity.com/paper/2206.08673/) (2022)

## Built on by (most cited first, 7 of 7)

- [Efficient Hate Speech Detection: Evaluating 38 Models from Traditional Methods to Transformers](https://mlsanity.com/paper/2509.14266/) (2025)
- [Advancing Mental Disorder Detection: A Comparative Evaluation of Transformer and LSTM Architectures on Social Media](https://mlsanity.com/paper/2507.19511/) (2025)
- [Beyond Architectures: Evaluating the Role of Contextual Embeddings in Detecting Bipolar Disorder on Social Media](https://mlsanity.com/paper/2507.14231/) (2025)
- [Two-Stage Voting for Robust and Efficient Suicide Risk Detection on Social Media](https://mlsanity.com/paper/2510.08365/) (2025)
- [A Benchmark Suite of Reddit-Derived Datasets for Mental Health Detection](https://mlsanity.com/paper/2604.23458/) (2026)
- [Validation-Gated Causal Interventions for Interpreting High-Stakes Large Language Model Behavior: A Case Study in Suicidality Detection](https://mlsanity.com/paper/2606.21078/) (2026)
- [Building Trustworthy Mental Health Benchmarks on Bluesky: A Validation-Aware Weak-Supervision Framework](https://mlsanity.com/paper/2609.22696/) (2026)

## Abstract (arXiv)

Suicide is a critical global health problem involving more than 700,000 deaths yearly, particularly among young adults. Many people express their suicidal thoughts on social media platforms such as Reddit. This paper evaluates the effectiveness of the deep learning transformer-based models BERT, RoBERTa, DistilBERT, ALBERT, and ELECTRA and various Long Short-Term Memory (LSTM) based models in detecting suicidal ideation from user posts on Reddit. Toward this objective, we curated an extensive dataset from diverse subreddits and conducted linguistic, topic modeling, and statistical analyses to ensure data quality. Our results indicate that each model could reach high accuracy and F1 scores, but among them, RoBERTa emerged as the most effective model with an accuracy of 93.22% and F1 score of 93.14%. An LSTM model that uses attention and BERT embeddings performed as the second best, with an accuracy of 92.65% and an F1 score of 92.69%. Our findings show that transformer-based models have the potential to improve suicide ideation detection, thereby providing a path to develop robust mental health monitoring tools from social media. This research, therefore, underlines the undeniable prospect of advanced techniques in Natural Language Processing (NLP) while improving suicide prevention efforts.