Deep Learning–based NLP Approaches for Depression Detection on Social Media Data: A Comparative Study

The detection of depression from social media text has become an important research topic in the field of Natural Language Processing (NLP). Online platforms such as Twitter and Reddit contain extensive user generated content where individuals frequently express emotions, personal struggles, and psychological states. These digital expressions provide meaningful linguistic patterns that can be analyzed to identify early signs of depression. This study presents a comparative analysis of several deep learning architectures for detecting depressive content in social media posts. The models evaluated include Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Bidirectional LSTM (BiLSTM), Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Graph Convolutional Networks (GCN). A standardized preprocessing pipeline involving text cleaning, normalization, and tokenization is applied to improve data quality before feature extraction using word embedding techniques. Model performance is measured using accuracy, precision, recall, and F1-score to ensure a comprehensive evaluation. In addition, a hybrid architecture combining BiLSTM and BiGRU layers is proposed to capture both long-term contextual dependencies and short-term sequential patterns within textual data. Experimental results indicate that the proposed hybrid model out performs baseline approaches in detecting depression related language. The findings highlight the importance of contextual representation and sequential learning in building reliable AI-based systems for early mental health monitoring.