Hybrid BERT-GRU Approach for Depression Detection on Social Media Post

Abstract

Depression is a severe mental ailment affecting millions of people worldwide. It has several negative consequences for society and the country, leading to societal deterioration. If not treated, the implications might be severe, including death. The use of social media platforms is rapidly growing. Twitter and Facebook are becoming platforms for depressed victims to express their feelings and emotions through textual content.  This paper evaluates the effectiveness of long short-term memory (LSTM), recurrent neural network (RNN), bidirectional long short-term memory (Bi-LSTM), bidirectional encoder representations from transformers (BERT), and gated recurrent unit (GRU). It also proposed an improved deep learning model based on a hybrid BERT-GRU approach. This study used deep learning techniques to analyse the combined Twitter and Facebook datasets to detect whether a tweet or post is depressive. Data preprocessing, extraction, text processing, and classification were performed. Experimental results based on various performance metrics indicate that BERT outperformed other techniques, such as LSTM, RNN, and Bi-LSTM, with 95.1% accuracy for depressive content identification. The findings also show that an improved hybrid BERT-GRU model proves to be a better model with 97.4% accuracy, proving that the hybrid model was efficient in identifying depressed and non-depressive text on Twitter and Facebook. The result indicates its superior ability to capture and interpret complex depression-related linguistic patterns, as evidenced by results obtained using multiple performance measures. This research will assist psychologists, policymakers, and other concerned members of society in identifying individuals who are vulnerable to depression and other mental health conditions among social media users.