Machine Learning Techniques for Early Detection of Mental Health Disorders Through Social Media Analysis

Abstract

The prevalence of mental health disorders has been escalating globally, prompting the need for innovative approaches to early detection and intervention. Social media platforms have emerged as rich sources of data reflecting individuals' psychological states. This paper explores various machine learning techniques employed to analyze social media data for the early detection of mental health disorders. By leveraging natural language processing (NLP), sentiment analysis, and deep learning models, researchers have developed systems capable of identifying indicators of conditions such as depression, anxiety, and bipolar disorder. The study reviews existing literature, outlines the methodological frameworks, and presents an analysis of the effectiveness of different algorithms. The findings suggest that while machine learning models show promise in early detection, challenges such as data privacy, ethical considerations, and the need for personalized approaches persist. The paper concludes by highlighting future directions for enhancing the accuracy and applicability of these techniques in real-world scenarios.