Early Detection of Mental Health Issues Using Social Media Posts
This work addresses the problem of early mental health crisis detection for individuals using social media, but it is incremental as it builds on existing methods with hybrid improvements.
The paper tackles early detection of mental health issues like depression and anxiety by proposing a multi-modal deep learning framework that integrates linguistic and temporal features from social media posts, achieving a validation accuracy of 74.55% and F1-Score of 0.7376.
The increasing prevalence of mental health disorders, such as depression, anxiety, and bipolar disorder, calls for immediate need in developing tools for early detection and intervention. Social media platforms, like Reddit, represent a rich source of user-generated content, reflecting emotional and behavioral patterns. In this work, we propose a multi-modal deep learning framework that integrates linguistic and temporal features for early detection of mental health crises. Our approach is based on the method that utilizes a BiLSTM network both for text and temporal feature analysis, modeling sequential dependencies in a different manner, capturing contextual patterns quite well. This work includes a cross-modal attention approach that allows fusion of such outputs into context-aware classification of mental health conditions. The model was then trained and evaluated on a dataset of labeled Reddit posts preprocessed using text preprocessing, scaling of temporal features, and encoding of labels. Experimental results indicate that the proposed architecture performs better compared to traditional models with a validation accuracy of 74.55% and F1-Score of 0.7376. This study presents the importance of multi-modal learning for mental health detection and provides a baseline for further improvements by using more advanced attention mechanisms and other data modalities.