Machine Learning Approaches for Identifying Early Signs of Mental Health Disorders Through Social Media

Abstract

The global rise in mental health disorders underscores the urgent need for innovative methods of early detection and intervention. Social media platforms have become valuable sources of data that offer insights into individuals' psychological well-being. This paper examines the application of various machine learning techniques in analyzing social media content for early identification of mental health conditions. Utilizing approaches such as natural language processing (NLP), sentiment analysis, and deep learning, researchers have developed models that can detect indicators of disorders like depression, anxiety, and bipolar disorder. The study reviews current literature, discusses methodological frameworks, and evaluates the effectiveness of various machine learning algorithms. Findings indicate that while these models show considerable potential for early detection, challenges including data privacy, ethical concerns, and the need for personalized solutions remain. The paper concludes with recommendations for advancing these techniques to improve their accuracy and real-world applicability.