DDNet: A Novel Hybrid Deep Learning Model for Detection and Classification of Depression in Social Media Conversations

In the modern world, people worldwide face different forms of depression due to factors such as workplace stress, economic pressures, and other causes. The rise of Artificial Intelligence (AI) has enabled data analysis and solving of real-world problems. People frequently use social media platforms to communicate and express their feelings. Hence, social media data is helpful for research purposes, particularly for automatic depression detection. Numerous scholarly works have explored using learning-based approaches to identify sadness from social media interactions. However, individual existing deep learning models have limitations, such as the inability to capture contextual and sequential dependencies in text fully. We addressed this by proposing a deep learning-based, non-invasive approach to identify depression in social media conversations. Our proposed approach involves a novel hybrid deep learning model, Depression Detection Network (DDNet), which combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. The model was trained and tested on a manually annotated dataset of 8500 depression-related tweets (6,800 for training and 1,700 for testing) collected via the Twitter Application Programming Interface (API). The DDNet model achieved a high accuracy of 96.21%, outperforming baseline models such as standalone LSTM (92.31%) and Recurrent Neural Network (RNN) (91.43%). Furthermore, we developed Hybrid Deep Learning-based Depression Detection (HDL-DD), an algorithm that processes social media text and predicts potential depressive tendencies. The experimental results indicate that DDNet significantly improves depression classification, achieving 95% precision, 96% recall, and 95% F1-score, demonstrating its effectiveness over existing methods. By recognizing depression with a 96.21% accuracy rate, our deep learning model outperformed previous state-of- the-art approaches, making it a promising tool for automated depression monitoring applications. This approach could be integrated into real-world social media-based mental health monitoring applications, supporting early intervention efforts and contributing to AI-driven healthcare solutions.