CDME-GAT: Context-Aware Depression Detection Using Multiembedding and Graph Attention Networks in Social Media Text

Depression, a prevalent mental health concern, requires timely identification and intervention. Automating the early stage identification of depression cues within social media text has become critically important. Existing methods for depression identification through text obtained from social media have shown promising results; however, these methods do not address the graphlike nature of social media data. To date, there has been little work addressing this problem by appropriately modeling social media data. This article aims to effectively harness the power of multiembedding techniques and graph attention networks (GATs) for depression identification. To overcome these limitations, the current study presents a novel methodology that combines multiple token-level embeddings, including BERT, RoBERTa, and DeBERTa, with GATs to leverage the graphlike nature of social media data and detect depression cues from such text. A dataset comprising 100 000 tweets has been curated using data from publicly available annotated tweet datasets. This dataset maintains a balanced distribution between depressive and nondepressive text samples. This was followed by a rigorous cleaning and preprocessing pipeline. Next, these data were transformed into a numeric feature matrix using multiple-word embeddings, which enabled the treatment of tweets as nodes in a graphlike structure. This graph was used for training a GAT model with multiple self-attentional layers, culminating in a linear layer for mapping traits to binary classes. The model presented in the current study achieves a precision of 97.2%, a recall of 96.4%, and an F1 score of 96.7% on a benchmark dataset.