Abstract
Due to the rising use of social media platforms on a global scale to interact and express thoughts freely, the spread of hate speech has become very noticeable on these platforms. Governments, organizations, and academic institutions have all spent substantially on discovering effective solutions to handle this issue. Numerous researches have been performed in several languages to find automated methods for identifying hate speech, but there has been minimal work done in Arabic. The findings of a performance evaluation of two machine learning models, namely the passive-aggressive classifier (PAC) and the Bidirectional Gated Recurrent Unit (Bi-GRU) augmented with an attention layer, are investigated in this work. Proposed models are developed and evaluated using a multi-platform Arabic hate speech dataset. We employ term frequency-inverse document frequency (TF-IDF) and Arabic word embeddings for feature extraction techniques after running a variety of pre-processing steps. The experimental results reveal that the two proposed models (PAC, Bi-GRU with attention layer) provide an accuracy of 98.4% and 99.1%, respectively, outperforming existing methods reported in the literature.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cortis K, Davis B (2020) Over a decade of social opinion mining. Springer, Netherlands. https://doi.org/10.1007/s10462-021-10030-2
Aljarah I, Habib M, Hijazi N, Faris H, Qaddoura R, Hammo B, Abushariah M, Alfawareh M (2020) Intelligent detection of hate speech in Arabic social network: a machine learning approach. J Inf Sci. https://doi.org/10.1177/0165551520917651
Jahan MS, Oussalah M (2021) A systematic review of Hate Speech automatic detection using Natural Language Processing (2021)
Salminen J, Hopf M, Chowdhury SA, Jung S, gyo, Almerekhi H, Jansen BJ (2020) Developing an online hate classifier for multiple social media platforms. Human-centric Comput Inf Sci 10:1–34. https://doi.org/10.1186/s13673-019-0205-6
Omar A, Mahmoud TM (2020) Comparative performance of machine learning and deep learning algorithms for Arabic Hate Speech detection in OSNs comparative performance of machine learning and deep learning algorithms for Arabic Hate Speech detection in OSNs. Springer International Publishing. https://doi.org/10.1007/978-3-030-44289-7
Husain F, Uzuner O (2021) A survey of offensive language detection for the Arabic Language. ACM Trans Asian Low-Resour Lang Inf Process 20:1–44. https://doi.org/10.1145/3421504
Abuzayed A, Elsayed T (2020) Quick and simple approach for detecting Hate Speech in Arabic Tweets. In: Proceedings of the 4th workshop on open-source Arabic Corpora and processing tools, with a shared task on offensive language detection, pp 109–114
Al-Hassan A, Al-Dossari H (2021) Detection of hate speech in Arabic tweets using deep learning. Multim Syst. https://doi.org/10.1007/s00530-020-00742-w
Hegazi MO, Al-Dossari Y, Al-Yahy A, Al-Sumari A, Hilal A (2021) Preprocessing Arabic text on social media. Heliyon. 7:e06191. https://doi.org/10.1016/j.heliyon.2021.e06191
Faris H, Aljarah I, Habib M, Castillo PA (2020) Hate speech detection using word embedding and deep learning in the Arabic Language Context Hate Speech detection using word embedding and deep learning in the Arabic Language context. https://doi.org/10.5220/0008954004530460
Al-Hassan A, Al-Dossari H (2019) Detection of hate speech in social networks: a survey on multilingual corpus, pp 83–100. https://doi.org/10.5121/csit.2019.90208
Haddad H, Mulki H, Oueslati A (2019) T-HSAB: A Tunisian Hate Speech and abusive dataset. Commun Comput Inf Sci 1108:251–263. https://doi.org/10.1007/978-3-030-32959-4_18
Mulki H, Haddad H, Bechikh Ali C, Alshabani H (2019) L-HSAB: a Levantine Twitter dataset for hate speech and abusive language, pp 111–118. https://doi.org/10.18653/v1/w19-3512
Albadi N, Kurdi M, Mishra S (2018) Are they our brothers? Analysis and detection of religious hate speech in the Arabic Twittersphere. In: Proceedings of the 2018 IEEE/ACM international conference on advances in social networks analysis and mining, ASONAM 2018, pp 69–76. https://doi.org/10.1109/ASONAM.2018.8508247
Elmadany A, Zhang C, Abdul-Mageed M, Hashemi A (2020) Leveraging affective bidirectional transformers for offensive language detection, pp 102–108
Mubarak H, Darwish K, Magdy W, Elsayed T, Al-Khalifa H (2020) Overview of {OSACT}4 {A}rabic offensive language detection shared task. In: Proceedings of the 4th workshop on open-source Arabic Corpora and processing tools, with a shared task on offensive language detection, pp 48–52
Hassan S, Samih Y, Mubarak H, Abdelali A, Rashed A, Chowdhury S (2020) ALT submission for OSACT shared task on offensive language detection, pp 61–65
Elzayady H, Badran KM, Salama GI (2019) Sentiment analysis on Twitter Data using Apache spark framework. In: Proceedings—2018 13th international conference on computer engineering and systems, ICCES 2018, pp 171–176. https://doi.org/10.1109/ICCES.2018.8639195
Gupta S, Meel P Passive-aggressive classifier. Springer, Singapore. https://doi.org/10.1007/978-981-15-7345-3
Nagashri K, Sangeetha J Passive-aggressive classifier and other machine learning algorithms. Springer, Singapore. https://doi.org/10.1007/978-981-33-6987-0
Li P, Luo A, Liu J, Wang Y, Zhu J, Deng Y, Zhang J (2020) Bidirectional gated recurrent unit neural network for Chinese address element segmentation. ISPRS Int J Geo-Information 9. https://doi.org/10.3390/ijgi9110635
Tay NC, Tee C, Ong TS, Teh PS (2019) Abnormal Behavior recognition using CNN-LSTM with attention mechanism. In: 2019 IEEE international conference on electrical, control and instrumentation engineering, ICECIE 2019—proceedings. https://doi.org/10.1109/ICECIE47765.2019.8974824
Haddad B, Orabe Z, Al-Abood A, Ghneim N (2020) {A}rabic offensive language detection with attention-based deep neural networks. In: Proceedings of the 4th workshop on open-source Arabic Corpora and processing tools, with a shared task on offensive language detection, pp 76–81
Mohaouchane H, Mourhir A, Nikolov NS (2019) Detecting offensive language on Arabic social media using deep learning. In: 2019 6th International conference on social networks analysis, management and security, SNAMS 2019, pp 466–471. https://doi.org/10.1109/SNAMS.2019.8931839
Acknowledgements
The authors would like to thank Dr. Ahmed Omar, From Computer Science Department, Faculty of Science, Minia University, for his support in providing us with the dataset.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Elzayady, H., Mohamed, M.S., Badran, K., Salama, G. (2023). Improving Arabic Hate Speech Identification Using Online Machine Learning and Deep Learning Models. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Seventh International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 448. Springer, Singapore. https://doi.org/10.1007/978-981-19-1610-6_46
Download citation
DOI: https://doi.org/10.1007/978-981-19-1610-6_46
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-1609-0
Online ISBN: 978-981-19-1610-6
eBook Packages: EngineeringEngineering (R0)