A Hybrid Temporal Deep Learning Framework for Real-Time Depression Detection on Social Media

Depression is one of the most common mental health problems, as it influences mood, cognition, and quality of life. Social media offers an abundance of user-generated content that can be utilized to detect and track depressive tendencies at early stages. The conventional methods cannot always work to capture very complex temporal variations and other hidden contextual indicators within the unstructured social media data. To overcome these drawbacks, this research proposed the Neuro Thought Weave Mindstream Predictor (NTwMP), an atomistic hybrid temporal Deep Learning (DL) model that combines both long-term and short-term temporal modeling with a ThoughtWeave Attention mechanism. The NTwMP enables categorization of multi-level severity, abrupt deviation detection, and understandable predictions, which are useful in preventing mental health interventions. The model handles dynamic social media feeds in real-time with sliding time windows, which does not require retraining. Accuracy is 98.33%, which is considerably higher than that of existing DL methods. Other measures such as precision, recall, and F1-score also show excellent reliability and consistency. The main advantages are scalability, reproducibility, interpretability, and applicability in real-time, which makes it a strong and viable instrument to monitor depression trends and assist clinicians, caregivers, and automated systems in making timely decisions and taking early action.