# Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning

arXiv: https://arxiv.org/abs/2207.01012 · HTML: https://mlsanity.com/paper/2207.01012/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 53 references that are in the map.
- First posted on arXiv 3 Jul 2022.

## Builds on (most cited first, 15 of 15)

- [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)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Transformers in Vision: A Survey](https://mlsanity.com/paper/2101.01169/) (2021)
- [Learning Deep Transformer Models for Machine Translation](https://mlsanity.com/paper/1906.01787/) (2019)
- [DeepViT: Towards Deeper Vision Transformer](https://mlsanity.com/paper/2103.11886/) (2021)
- [Very Deep Transformers for Neural Machine Translation](https://mlsanity.com/paper/2008.07772/) (2020)
- [Small and Practical BERT Models for Sequence Labeling](https://mlsanity.com/paper/1909.00100/) (2019)
- [A Survey on Recent Advances in Sequence Labeling from Deep Learning Models](https://mlsanity.com/paper/2011.06727/) (2020)
- [Adapting Deep Learning Methods for Mental Health Prediction on Social Media](https://mlsanity.com/paper/2003.07634/) (2020)
- [Explainable Multi-class Classification of the CAMH COVID-19 Mental Health Data](https://mlsanity.com/paper/2105.13430/) (2021)
- [Detection of COVID-19 informative tweets using RoBERTa](https://mlsanity.com/paper/2010.11238/) (2020)

## Built on by (most cited first, 5 of 5)

- [Depression detection in social media posts using transformer-based models and auxiliary features](https://mlsanity.com/paper/2409.20048/) (2024)
- [Advancing Mental Disorder Detection: A Comparative Evaluation of Transformer and LSTM Architectures on Social Media](https://mlsanity.com/paper/2507.19511/) (2025)
- [The (In)Effectiveness of Intermediate Task Training For Domain Adaptation and Cross-Lingual Transfer Learning](https://mlsanity.com/paper/2210.01091/) (2022)
- [UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensemble of BERTs for Classifying Common Mental Illnesses on Social Media Posts](https://mlsanity.com/paper/2304.04539/) (2023)
- [MentalMARBERT: Domain-Adaptive Pre-training and Two-Stage Fine-Tuning for Arabic Mental Health Disorders Detection](https://mlsanity.com/paper/2606.12649/) (2026)

## Abstract (arXiv)

Given the current social distance restrictions across the world, most individuals now use social media as their major medium of communication. Millions of people suffering from mental diseases have been isolated due to this, and they are unable to get help in person. They have become more reliant on online venues to express themselves and seek advice on dealing with their mental disorders. According to the World health organization (WHO), approximately 450 million people are affected. Mental illnesses, such as depression, anxiety, etc., are immensely common and have affected an individuals' physical health. Recently Artificial Intelligence (AI) methods have been presented to help mental health providers, including psychiatrists and psychologists, in decision making based on patients' authentic information (e.g., medical records, behavioral data, social media utilization, etc.). AI innovations have demonstrated predominant execution in numerous real-world applications broadening from computer vision to healthcare. This study analyzes unstructured user data on the Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD. We trained traditional machine learning, deep learning, and transfer learning multi-class models to detect mental disorders of individuals. This effort will benefit the public health system by automating the detection process and informing appropriate authorities about people who require emergency assistance.