Explainable Deep Learning for Mental Health Detection from English and Arabic Social Media Posts

Abstract

The research communities have recently begun exploring the detection of depression through social media, making it a relatively new development in this field. Some research has been done to identify depressive signs from English social media postings, while little work has been done for the Arabic posts. This paper proposes a BERT and Bi-LSTM pipeline for the identification of depressive signs from Arabic social media posts. A fine-tuned RoBERTa-based model is also proposed for English social media posts for depressive state identification. Along with the proposed model, seven conventional machine learning and eight deep learning models are also explored for the identification of depressive signs from Arabic and English social media posts. The performance of the proposed model is validated on two Arabic datasets and one English dataset. The proposed BERT and Bi-LSTM pipeline achieved state-of-the-art performance with an F 1 -score of 1.00 and 0.82 for two different Arabic datasets, whereas the proposed fine-tuned RoBERTa achieved a F 1 -score of 0.60 which is comparable in identifying depressive sign from English social media posts. The majority of the suggested deep learning models are end-to-end, which necessitates a greater explanation for their success. An explainable AI-based model may enhance decision-making, transparency, and interpretability. Therefore, this research identifies where the suggested system learned well and where it failed in recognition of the depression signs that can help in future developments in the field of depression detection.