MOOD MAPPING IN THE DIGITAL WORLD: ML AND DL-BASED INSIGHTS INTO DEPRESSION DETECTION ON SOCIAL PLATFORM

Abstract

The widespread adoption of social media platforms has transformed digital communication by enabling individuals to share thoughts, emotions, and daily experiences in real time. These online interactions generate valuable textual, visual, and behavioral data that can provide meaningful insights into users’ mental health conditions. Depression is one of the most prevalent mental health disorders worldwide, and early identification is crucial for timely intervention and improved psychological well-being. Conventional depression diagnosis primarily depends on clinical interviews, psychological assessments, and self-reported questionnaires, which may delay detection and limit continuous monitoring. Recent advancements in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have enabled automated analysis of large-scale social media data for intelligent mental health assessment. This paper proposes an ML and DL-based framework for depression detection using social platform data by integrating natural language processing, sentiment analysis, behavioral feature extraction, and deep neural networks. The proposed framework analyzes textual posts, user engagement patterns, emotional expressions, and linguistic features to identify depressive tendencies. Comparative evaluation using traditional machine learning algorithms and advanced deep learning models demonstrates that deep learning significantly improves prediction accuracy, precision, recall, and early depression identification. The proposed system provides an intelligent decision-support framework that can assist healthcare professionals in large-scale mental health monitoring while preserving scalability and efficiency. This research contributes to the advancement of AI-driven digital mental healthcare by enabling early depression detection through intelligent social media analytics.