Hierarchical Explainable Network for Investigating Depression From Multilingual Textual Data

Depression is the most prevalent mental health disorder, which is spreading like an epidemic. With the rise of the internet, people from all over the world now express their daily lives on social media, which can be utilized to analyze their mental health condition. Automated approaches like Machine Learning (ML) and Deep Learning (DL) are used to analyze social media posts. The ML techniques are performing well in detecting depression from monolingual text. Nevertheless, ML model’s low interpretability makes it difficult to identify depression in multilingual texts. Hence, the study focuses on seven languages for developing the model. Traditional ML and DL algorithms like Convolutional Neural Network (CNN), Support Vector Machine, Random Forest, and Decision Tree lack interpretability regarding prediction.…