# Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks

arXiv: https://arxiv.org/abs/2306.16891 · HTML: https://mlsanity.com/paper/2306.16891/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 49 references that are in the map.
- First posted on arXiv 29 Jun 2023.

## Builds on (most cited first, 12 of 12)

- [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)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare](https://mlsanity.com/paper/2110.15621/) (2021)
- [Comparing BERT against traditional machine learning text classification](https://mlsanity.com/paper/2005.13012/) (2020)
- [Early Risk Detection of Pathological Gambling, Self-Harm and Depression Using BERT](https://mlsanity.com/paper/2106.16175/) (2021)
- [DEPTWEET: A Typology for Social Media Texts to Detect Depression Severities](https://mlsanity.com/paper/2210.05372/) (2022)
- [Calibration of Transformer-based Models for Identifying Stress and Depression in Social Media](https://mlsanity.com/paper/2305.16797/) (2023)
- [Adapting Deep Learning Methods for Mental Health Prediction on Social Media](https://mlsanity.com/paper/2003.07634/) (2020)
- [Predicting mental health using social media: A roadmap for future development](https://mlsanity.com/paper/2301.10453/) (2023)
- [Depression detection in social media posts using affective and social norm features](https://mlsanity.com/paper/2303.14279/) (2023)

## Built on by (most cited first, 3 of 3)

- [Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practice](https://mlsanity.com/paper/2501.00982/) (2025)
- [MentalMARBERT: Domain-Adaptive Pre-training and Two-Stage Fine-Tuning for Arabic Mental Health Disorders Detection](https://mlsanity.com/paper/2606.12649/) (2026)
- [Evaluating Document-Tuned Transformer Representations for Person-level Mental Health Assessment](https://mlsanity.com/paper/2606.21622/) (2026)

## Abstract (arXiv)

Early diagnosis of mental disorders and intervention can facilitate the prevention of severe injuries and the improvement of treatment results. Using social media and pre-trained language models, this study explores how user-generated data can be used to predict mental disorder symptoms. Our study compares four different BERT models of Hugging Face with standard machine learning techniques used in automatic depression diagnosis in recent literature. The results show that new models outperform the previous approach with an accuracy rate of up to 97%. Analyzing the results while complementing past findings, we find that even tiny amounts of data (like users' bio descriptions) have the potential to predict mental disorders. We conclude that social media data is an excellent source of mental health screening, and pre-trained models can effectively automate this critical task.