Mental Health Safety and Depression Detection in Social Media Text Data: A Classification Approach Based on a Deep Learning Model

Social media expansion enables real-time mental health monitoring particularly for depression detection through its platforms. Traditional depression detection techniques, however, struggle to handle the informal, often ambiguous nature of language used in social media, including emojis, slang, and context-dependent expressions. To address this limitation, this research proposes a novel framework for depression detection that integrates advanced text preprocessing with a deep learning model. The preprocessing pipeline contains two key components where emojis are converted into text by emoji normalization then slang expressions are replaced through a customized dictionary. Emotion scores serve as additional components to identify depression-related emotional cues within the text. The model combines BERT’s contextual embeddings with BiLSTM’s sequential processing power to effectively represent emotional content in social media posts. The proposed approach demonstrates superior performance compared to traditional baseline models by achieving better results in accuracy, precision, recall, F1-score, AUC and RGA. This approach shows exceptional performance in detecting depression through informal texts which serves as a new benchmark for depression detection in dynamic social media environments.