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

TL;DR

This paper explores the use of deep learning and transfer learning models to classify five common mental illnesses from social media texts, aiming to assist mental health detection and intervention.

Contribution

It introduces a novel approach combining deep learning and transfer learning for mental illness classification on social media data, specifically Reddit posts.

Findings

Deep learning models outperform traditional machine learning in classification accuracy.

Transfer learning enhances model performance with limited labeled data.

The approach can effectively identify mental health issues from unstructured social media texts.

Abstract

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.