# Advancing Mental Disorder Detection: A Comparative Evaluation of Transformer and LSTM Architectures on Social Media

arXiv: https://arxiv.org/abs/2507.19511 · HTML: https://mlsanity.com/paper/2507.19511/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 48 references that are in the map.
- First posted on arXiv 17 Jul 2025.

## 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)
- [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)
- [Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks](https://mlsanity.com/paper/2103.14749/) (2021)
- [Topic Modeling in Embedding Spaces](https://mlsanity.com/paper/1907.04907/) (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)
- [SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions](https://mlsanity.com/paper/1806.05258/) (2018)
- [A Comparative Analysis of Transformer and LSTM Models for Detecting Suicidal Ideation on Reddit](https://mlsanity.com/paper/2411.15404/) (2024)
- [Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning](https://mlsanity.com/paper/2207.01012/) (2022)
- [A Survey-Based Qualitative Study to Characterize Expectations of Software Developers from Five Stakeholders](https://mlsanity.com/paper/2107.09587/) (2021)

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

- [Mental Multi-class Classification on Social Media: Benchmarking Transformer Architectures against LSTM Models](https://mlsanity.com/paper/2509.16542/) (2025)
- [A Benchmark Suite of Reddit-Derived Datasets for Mental Health Detection](https://mlsanity.com/paper/2604.23458/) (2026)

## Abstract (arXiv)

The rising prevalence of mental health disorders necessitates the development of robust, automated tools for early detection and monitoring. Recent advances in Natural Language Processing (NLP), particularly transformer-based architectures, have demonstrated significant potential in text analysis. This study provides a comprehensive evaluation of state-of-the-art transformer models (BERT, RoBERTa, DistilBERT, ALBERT, and ELECTRA) against Long Short-Term Memory (LSTM) based approaches using different text embedding techniques for mental health disorder classification on Reddit. We construct a large annotated dataset, validating its reliability through statistical judgmental analysis and topic modeling. Experimental results demonstrate the superior performance of transformer models over traditional deep-learning approaches. RoBERTa achieved the highest classification performance, with a 99.54% F1 score on the hold-out test set and a 96.05% F1 score on the external test set. Notably, LSTM models augmented with BERT embeddings proved highly competitive, achieving F1 scores exceeding 94% on the external dataset while requiring significantly fewer computational resources. These findings highlight the effectiveness of transformer-based models for real-time, scalable mental health monitoring. We discuss the implications for clinical applications and digital mental health interventions, offering insights into the capabilities and limitations of state-of-the-art NLP methodologies in mental disorder detection.