MoodMirror

BSc (Hons) Computer Science | Final Year Project

A.M.Kavindu Pramodya Abeysundara

Mental health conditions such as depression and anxiety are increasingly reflected in how people behave and communicate on social media platforms. However, most existing automated detection systems focus only on what users write, ignoring important behavioral patterns such as when they post, how often, and where. This study proposes a hybrid deep learning framework that classifies Reddit users into at-risk and severe mental health risk categories by combining both text and behavioral signals. A fine-tuned Bidirectional Encoder Representations from Transformers (BERT) encoder captures the meaning behind user posts, while a two-layer Bidirectional Long Short Term Memory (BiLSTM) with attention models patterns in user behavior over time. The key contribution of this work is the fusion of these two complementary signal types – linguistic and behavioral – into a unified multi-factor severity scoring system, enabling more personalized and context-aware risk assessment. The model is trained on a balanced dataset of 3,500 Reddit users and achieves an accuracy of 82.49%, precision of 76.44%, recall of 92.97%, F1-score of 83.90%, Area Under the Receiver Operating Characteristic Curve (AUC ROC) of 91.36%, sensitivity of 92.97%, and specificity of 72.40% at an optimized threshold of 0.380. These results show that incorporating behavioral context alongside text significantly improves identifying behavioral risk patterns associated with mental health, with potential to support early intervention efforts in community-facing early awareness settings.