Social Media-Driven Depression Detection Using Improved Recurrent Neural Architecture Leveraging GloVe Word Embeddings - Siddiqui, Tariq; Pandey, Ashish (Journal of Neonatal Surgery, 2025)
The increasing incidence of mental health disorders, especially depression in modern society highlights the immediate need for effective methods to identify and reduce these issues immediately. This study introduces an advanced recurrent nervous network (RNN) model to classify depression based on lesson data obtained from social media platforms, which has greatly improved by incorporating the gloves (global archives for word representation) embeding. The use of gloves embeding provides the model enhanced semantics and relevant insights, which facilitates more intensive understanding of micro-linguistic elements found in social media communication related to depression. The model trained and evaluated using reddit dataset. Experimental conclusions revealed exceptional display matrix, with a test accuracy of 97.22%. These results display the proficiency of the model in correcting manifestations related to depression while maintaining both high sensitivity and uniqueness. By merging linguistic understanding with machine learning, the proposed structure presents a viable result for the initial identity of depression through public platform media text analysis