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

TL;DR

This paper demonstrates that pre-trained Hugging Face BERT models can accurately predict mental health disorders from social media data, outperforming traditional methods with up to 97% accuracy, even with minimal user data.

Contribution

It introduces the use of advanced transformer models for mental health prediction from social media, showing significant improvements over existing approaches.

Findings

Transformer models outperform traditional methods in accuracy.

Minimal data like user bios can predict mental disorders.

Social media is a valuable source for mental health screening.

Abstract

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.