02320nam a22001577a 4500 610 Liaquat, Umna 132918 Social Media Analytics for Mental Health Assessment / Umna Liaquat Islamabad : SMME- NUST; 2025. 94p. Soft Copy 30cm Social media has emerged as a tool for exploring the human psyche, offering exceptional access to real-time behavioral signals that are transforming the landscape of computational psychiatry. Among these, bipolar disorder is difficult to detect because of its volatility, requiring robust modeling of emotion, behavior, and timing. Prior studies have largely focused on text-based sentiment analysis and linguistic features to classify mental health conditions; however, these approaches often neglect non-verbal markers such as circadian rhythms and affective variability. Existing models primarily depend on static textual cues, limiting their ability to capture the dynamic, multimodal nature of psychiatric expression. This study addresses these limitations by integrating temporal rhythms, emotional dynamics, and behavioral signals extracted from Reddit user histories to develop predictive models of bipolar disorder and high-risk psychological states. We propose a series of interpretable multimodal architectures employing classical machine learning (Logistic regression, Random Forest, and XGBoost), deep sequence models (LSTM, GRU), and transformer-based frameworks (Roberta, GPT). Our approach incorporates temporal posting features, emotional entropy, and community-level interaction structures. Compared to benchmark studies, our models demonstrate significant improvements in both classification (F1 > 0.99) and regression (R² > 0.89), highlighting the predictive power of fused behavioral signals. This work advances the field by providing a scalable, languageindependent framework for the early detection of psychiatric risk. It also holds broader implications for public health by offering a foundation for real-time, ethically deployable digital mental health tools. MS Biomedical Engineering (BME) 119509 Supervisor : Dr. Nabeel Anwar 120667 http://10.250.8.41:8080/xmlui/handle/123456789/57289 ddc THE 615943 615943 0 0 0 0 SMME SMME EB 2026-01-26 0 610 SMME-TH-1206 2026-01-26 2026-01-26 THE