Detection of Depression Among Arabic Twitter Users Using a Convolutional Neural Network

Mohammed Majid Msallam; Esraa Yahia Tarkan; Sarah Kareem Salim

doi:10.46604/peti.2026.16024

Authors

Mohammed Majid Msallam College of Artificial Intelligence Engineering, University of Technology, Baghdad, Iraq
Esraa Yahia Tarkan Ministry of Higher Education and Scientific Research, Baghdad, Iraq
Sarah Kareem Salim Electrical Engineering Department, University of Misan, Misan, Iraq

DOI:

https://doi.org/10.46604/peti.2026.16024

Keywords:

convolutional neural network, depression, prevention, Arabic tweets, mental health

Abstract

Depression is a prevalent mental health condition, where early detection using the analysis of patients' feelings contributes to preventing and treating the exacerbation of symptoms. This work aims to detect depression in Arabic tweets using convolutional neural networks (CNN) trained on a dataset generated from the Arabic_Dep_tweets_10,000 dataset. Two datasets are generated using the stripping and captioning process to improve the generalization of the proposed CNN model. The stripping process removes punctuation marks from Arabic tweets, while the captioning process types the Arabic tweet on a white background to generate an image. Experimental results demonstrate that the proposed CNN model achieves up to 100% accuracy on the generated dataset treated with stripping and the captioning process.

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