Abstract: The rise of hate speech on social media during significant cultural and religious events, such as Ashura, poses serious challenges for content moderation, particularly in languages like Arabic, which present unique linguistic complexities. Most existing hate speech detection models, primarily developed for English text, fail to effectively handle the intricacies of Arabic, including its diverse dialects and rich morphology. This limitation underscores the need for specialized models tailored to the Arabic language. In response, the CNN-BiGRU-Focus model proposed, a novel hybrid deep learning (DL) approach that combines Convolutional Neural Networks (CNN) to capture local linguistic patterns and Bidirectional Gated Recurrent Units (BiGRU) to manage long-term dependencies in sequential text. An attention mechanism is incorporated to enhance the model's ability to focus on the most relevant sections of the input, improving both the accuracy and interpretability of its predictions. In this paper, this model applied to a dataset of social media posts related to Ashura, revealing that 32% of the content comprised hate speech, with Shia users expressing more sentiments than Sunni users. Through extensive experiments, the CNN-BiGRU-Focus model demonstrated superior performance, significantly outperforming baseline models. It achieved an accuracy of 99.89% and AUC of 99, marking a substantial improvement in Ashura-Arabic hate speech detection. The model effectively addresses the linguistic challenges of Arabic, including dialect variations and nuanced contexts, making it highly suitable for content moderation tasks. To the best of author’s knowledge, this study represents the first attempt to compile an Arabic-Ashura hate detection dataset from Twitter and apply CNN-BiGRU-Focus DL model to detect hate sentiment in Arabic social media posts. Dataset and source code can be downloaded from (https://github.com/imamu-asa).

Sarah Omar Alhumoud, “CNN-BiGRU-Focus: A Hybrid Deep Learning Classifier for Sentiment and Hate Speech Analysis of Ashura-Arabic Content for Policy Makers” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151199

@article{Alhumoud2024,
title = {CNN-BiGRU-Focus: A Hybrid Deep Learning Classifier for Sentiment and Hate Speech Analysis of Ashura-Arabic Content for Policy Makers},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151199},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151199},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Sarah Omar Alhumoud}
}