An evaluation dataset for depression detection in Arabic social media Online publication date: Wed, 22-Dec-2021
by Somaia Elimam; Mohamed Bougeussa
International Journal of Knowledge Engineering and Data Mining (IJKEDM), Vol. 7, No. 1/2, 2021
Abstract: Studying depression in Arabic social media has been neglected compared to other languages and the traditional way of dealing with depression (face-to-face medical diagnose) is not enough as the number of people that suffer from depression in Arabic communities increased dramatically. This paper proposes the first dataset to detect depressed users in Arabic social media. We pondered tweets from Twitter, pre-processed and converted it to a structured format. A notable advantage of the elaborated dataset is that it allows effective evaluation of machine learning algorithms for depression detection. We employ several classification algorithms such as deep neural network, logistic regression, multinomial Naïve Bayes, Bernoulli Naïve Bayes, AdaBoost, passive aggressive, nearest centroid, and linear SVC. The F-score, AUC, precision, and accuracy scores were selected as performance measures to compare algorithms, and the result showed that it is very challenging to classify Arabic tweets especially with the sparse nature of Twitter data.
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