Multi-modal deep-attention-BiLSTM based early detection of mental health issues using social media posts.

Résumé

The rising prevalence of mental health disorders such as depression, anxiety, and bipolar disorder underscores the urgent need for effective tools to enable early detection and intervention. Social media platforms like Reddit offer a rich source of user-generated content that reflects emotional and behavioral patterns, making them valuable for mental health analysis. However, many existing social media-based approaches focus solely on textual or audiovisual features, often overlooking temporal posting behaviors that can provide crucial contextual cues. Addressing this gap, this study proposes a multi-modal deep learning framework that integrates both linguistic and temporal features from social media posts to detect early signs of mental health crises. The proposed architecture, named DABLNet, utilizes social media post text and timestamp information as input to model the sequential dependencies between user behavior and various mental health conditions. DABLNet consists of a Bi-directional LSTM (BiLSTM) to process textual content, an LSTM to process its temporal data, a cross-modal attention module to fuse outputs from both networks, and a dense layer for classification. This fusion enables context-aware prediction of mental health states. The model is trained and evaluated on a dataset of labeled Reddit posts, which were preprocessed through text cleaning, temporal feature scaling, and label encoding. Experimental results show that the proposed network outperforms traditional models, achieving a test accuracy of 75.96% and an F1-score of 73.76%. These findings highlight the benefits of combining temporal dynamics alongside textual information and provide a solid foundation for future improvements that use more sophisticated attention mechanisms or new data modalities.