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Abstract
Nowadays, people are communicating through social networks everywhere. However, for whatever reason it is noticeable that verbal misbehaviors, such as hate speech is now propagated through the social networks. One of the most popular social networks is Twitter which has gained widespread in the Arabic region. This research aims to identify and classify Arabic tweets into 5 distinct classes: none, religious, racial, sexism or general hate. A dataset of 11 K tweets was collected and labelled and SVM model was used as a baseline to be compared against 4 deep learning models: LTSM, CNN + LTSM, GRU and CNN + GRU. The results show that all the 4 deep learning models outperform the SVM model in detecting hateful tweets. Although the SVM achieves an overall recall of 74%, the deep learning models have an average recall of 75%. However, adding a layer of CNN to LTSM enhances the overall performance of detection with 72% precision, 75% recall and 73% F1 score.
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Acknowledgements
The authors would like to thank Deanship of scientific research in King Saud University, for funding and supporting this research through the initiative of DSR Graduate Students Research Support (GSR).
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Information Systems Department, College of Computer Science and Information Systems, King Saud University, Riyadh, Saudi Arabia
Areej Al-Hassan & Hmood Al-Dossari
- Areej Al-Hassan
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- Hmood Al-Dossari
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Correspondence toAreej Al-Hassan.
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Al-Hassan, A., Al-Dossari, H. Detection of hate speech in Arabic tweets using deep learning.Multimedia Systems28, 1963–1974 (2022). https://doi.org/10.1007/s00530-020-00742-w
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