Citation

Al-Hassan A, Al-Dossari H. Multimed. Syst. 2021.

Copyright

(Copyright © 2021, Association for Computing Machinery, Publisher Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s00530-020-00742-w

PMID

unavailable

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.

Language: en

Keywords

Arabic NLP; Arabic tweets; Deep learning; Hate speech; Multiclassification; Social networks; Text mining