• Y Venkata SreeRanga Anil
• Dr. M. Vikram

Mental health disorders are increasingly prevalent in today’s digital society, with social media platforms becoming a rich source of real-time behavioral data. This study aims to develop an intelligent system that can detect and predict the risk of future mental health issues by analyzing user-generated content on platforms like Twitter, Reddit, and Facebook. The proposed framework integrates machine learning, ensemble learning techniques (such as Random Forest, XGBoost, and Voting Classifier), and advanced Large Language Models (LLMs) to understand linguistic, emotional, and psychological patterns. Feature extraction is carried out using Natural Language Processing (NLP) methods, including sentiment analysis, topic modeling, and embedding-based representations (e.g., BERT, RoBERTa). Ensemble models are trained to identify early signs of depression, anxiety, or stress. LLMs enhance contextual understanding and improve classification accuracy. This hybrid approach not only boosts performance but also ensures interpretability and robustness. The model can serve as an early warning tool for mental health professionals, enabling proactive support and intervention based on users' online behavior. In recent years, the adoption of Large Language Models (LLMs) like GPT and BERT has transformed the landscape of mental health analysis by enabling nuanced understanding of language, sentiment, and context. These models can capture subtle emotional cues, sarcasm, or self-referential language often missed by traditional algorithms. By fine-tuning LLMs on labeled datasets related to mental health, the system can recognize early warning signs such as hopelessness, emotional withdrawal, or aggression—patterns that may indicate conditions like depression, anxiety, bipolar disorder, or suicidal ideation. This deep linguistic comprehension adds a critical layer of intelligence to the prediction pipeline. To enhance accuracy and generalization, ensemble learning methods are employed to combine the predictions from multiple base learners. Models like Random Forest, AdaBoost, and Gradient Boosting are integrated through majority voting or stacking strategies. These ensemble models reduce bias and variance, allowing the system to perform reliably across diverse users and platforms. Additionally, explainable AI techniques are incorporated to highlight the specific phrases or features influencing the model's decision, thereby making the predictions more transparent and clinically interpretable. The ultimate goal of this work is to provide a scalable, real-time mental health monitoring solution that bridges the gap between online behavior and proactive psychological care.

Mental Health Prediction, Social Media Analysis, Machine Learning, Ensemble Learning, Large Language Models (LLMs), Natural Language Processing (NLP), Depression Detection.

"Social Signals and the Psyche: Forecasting Mental Health Disorders via Online Behavior", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.g321-g327, July-2025, Available :http://www.jetir.org/papers/JETIR2507647.pdf

"Social Signals and the Psyche: Forecasting Mental Health Disorders via Online Behavior", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. ppg321-g327, July-2025, Available at : http://www.jetir.org/papers/JETIR2507647.pdf