A MACHINE LEARNING APPROACH FOR EARLY MENTAL HEALTH RISK DETECTION VIA SOCIAL MEDIA ANALYTICS

Abstract

The rapid growth of social media platforms has generated vast amounts of user-generated content that can provide valuable insights into individuals’ mental health conditions. Early identification of mental health risks such as depression, anxiety, stress, and suicidal ideation is essential for timely intervention and preventive healthcare. This paper presents a machine learning-based framework for early mental health risk detection using social media analytics. The proposed system collects textual data from social media platforms and applies natural language processing (NLP) techniques for data preprocessing, sentiment analysis, feature extraction, and behavioral pattern identification. Various machine learning algorithms, including Support Vector Machine (SVM), Random Forest, Naïve Bayes, and Deep Learning models, are utilized to classify users based on mental health risk levels. The framework analyzes linguistic patterns, emotional expressions, posting frequency, and user engagement metrics to improve prediction accuracy. Experimental results demonstrate that the proposed approach achieves higher detection performance, improved accuracy, and faster prediction compared to traditional manual assessment methods. The system can assist healthcare professionals, counselors, and support organizations in identifying high-risk individuals at an early stage and enabling proactive mental health intervention. Furthermore, the integration of explainable AI techniques enhances the transparency and reliability of the prediction process, making the system more suitable for real-world mental healthcare applications.