SOCIALMIND: UNDERSTANDING PSYCHOLOGICAL WELL-BEING THROUGH LANGUAGE PATTERNS IN ONLINE DISCOURSE

Abstract

The rapid rise of social media platforms has revolutionized human communication, offering real-time insights into users’ emotions, thoughts, and behaviors. However, this digital expression has also exposed growing concerns regarding mental health, stress, and emotional instability among online communities. This study introduces SocialMind, a computational linguistics-based framework that analyzes social media language patterns to assess users’ psychological well-being. By leveraging natural language processing (NLP), sentiment analysis, and machine learning algorithms, the framework identifies linguistic markers associated with depression, anxiety, and emotional distress. SocialMind employs advanced models such as BERT and LSTM to detect subtle linguistic cues—tone, polarity, word frequency, and emotional context—within social media posts. Experimental results demonstrate that SocialMind can accurately predict mental health tendencies while preserving user anonymity. The proposed approach aims to support mental health professionals in early detection and intervention strategies, contributing to a more empathetic and data-driven understanding of mental wellness in the digital era.