Deep Learning for Detecting Mental Disorder from User Content on Social Media

The prevalence of mental health disorders is rising, with many individuals expressing their thoughts and feelings through social media posts. This presents an opportunity for early detection of mental conditions using natural language processing (NLP). This paper addresses the lack of effective tools for detecting mental disorders from Arabic social media content. We propose Ara-MentalBERT, a novel deep learning fusion model that combines AraBERT and MentalBERT using a deep joint autoencoder (DJAE). The DJAE model aims to integrate Arabic language understanding with specialized mental health domain knowledge by fusing Arabic and mental health embedding representations of text. Our proposed method involves three key steps: (1) fine-tuning pre-trained AraBERT and MentalBERT models, (2) employing a deep joint autoencoder for Ara-Mental text representation learning, and (3) utilizing a mental health disorder classifier with the fused Ara-Mental representations. We evaluate our model on two diverse datasets: a Reddit dataset and a Twitter dataset. Results demonstrate significant improvements over existing methods, achieving an accuracy of 81.12% on the Reddit dataset for combined titles and posts and 86.12% accuracy on the Arabic tweets. These findings highlight the potential of our model to enhance mental health detection in Arabic-speaking communities, offering a powerful tool for identifying at-risk individuals more effectively. Our proposed method makes valuable progress at the intersection of NLP, mental health, and deep learning, paving the way for Arabic social media monitoring to facilitate early intervention and improve mental healthcare. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.