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Detection of hate speech in Arabic tweets using deep learning

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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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References

  1. Salem, F.: Arab Social Media Report : Social Media and the Internet of Things: Towards Data-Driven Policymaking in the Arab World - Potential, Limits and Concerns, MBR School of Goverment 7, (2017). https://www.mbrsg.ae/home/publications/research-report-research-paper-white-paper/arab-social-media-report-2017.aspx

  2. Blaya, C.: Cyberhate: A review and content analysis of intervention strategies. Aggress. Violent Behav. 45, 0–1 (2018)

  3. Gelashvili, T., Nowak, K.A.: Hate Speech on Social Media. Lund University (2018)

  4. Fortuna, P., Nunes, S.: A survey on automatic detection of hate speech in text. ACM Comput. Surv. 51(4), 1–30 (2018)

    Article  Google Scholar 

  5. Waseem Z., Hovy, D.: Hateful symbols or hateful people? Predictive features for hate speech detection on twitter. In: Proc. NAACL Student Res. Work., pp. 88–93 (2016). https://www.aclweb.org/anthology/N16-2013/

  6. Anis M.Y., Maret, U.S.: Hatespeech in Arabic Language. In: International Conference on Media Studies, September 2017

  7. Alshutayri A., Atwell, E.: Creating an Arabic Dialect Text Corpus by Exploring Twitter, Facebook, and Online Newspapers, May 2018

  8. Irfan, R., et al.: A survey on text mining in social networks. Knowl. Eng. Rev. 30(2), 157–170 (2015)

    Article  Google Scholar 

  9. Assiri, A., Emam, A., Al-Dossari, H.: Towards enhancement of a lexicon-based approach for Saudi dialect sentiment analysis. J. Inf. Sci. 44(2), 184–202 (2018)

    Article  Google Scholar 

  10. Soumya George, K., Joseph, S.: Text classification by augmenting bag of words (BOW) representation with co-occurrence feature. IOSR J. Comput. Eng. 16(1), 34–38 (2014)

    Article  Google Scholar 

  11. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  12. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)

  13. Soliman, A.B., Eissa, K., El-Beltagy, S.R.: AraVec: a set of Arabic word embedding models for use in Arabic NLP. Procedia Comput. Sci. 117, 256–265 (2017)

    Article  Google Scholar 

  14. Bouazizi, M., Otsuki, T.: A pattern-based approach for sarcasm detection on twitter. IEEE Access 4, 5477–5488 (2016)

    Article  Google Scholar 

  15. Xiang, G., Fan, B., Wang, L., Hong, J., Rose, C.: Detecting offensive tweets via topical feature discovery over a large scale twitter corpus. In: Proc. 21st ACM Int. Conf. Inf. Knowl. Manag.—CIKM’12, pp 1980 (2012)

  16. Gitari, N.D., Zuping, Z., Damien, H., Long, J.: A lexicon-based approach for hate speech detection. Int. J. Multimed. Ubiquitous Eng. 10(4), 215–230 (2015)

    Article  Google Scholar 

  17. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)

    MATH  Google Scholar 

  18. Warner W., Hirschberg, J.: Detecting Hate Speech on the World Wide Web. In: Proceedings of the Second Workshop on Language in Social Media, pp. 19–26 (2012)

  19. Watanabe, H., Bouazizi, M., Ohtsuki, T.: Hate speech on twitter: a pragmatic approach to collect hateful and offensive expressions and perform hate speech detection. IEEE Access 6, 13825–13835 (2018)

    Article  Google Scholar 

  20. Burnap, P., Williams, M.L.: Us and them: identifying cyber hate on Twitter across multiple protected characteristics. EPJ Data Sci (2016). https://doi.org/10.1140/epjds/s13688-016-0072-6

    Article  Google Scholar 

  21. Gambäck, B., Sikdar, U.K.: Using convolutional neural networks to classify hate-speech. Assoc. Comput. Linguist. 7491, 85–90 (2017)

    Google Scholar 

  22. Badjatiya P., Gupta S., Gupta, M., Varma, V.: Deep learning for hate speech detection in tweets. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 759–760 (2017)

  23. Zhang, Z., Robinson, D., Tepper, J.: Detecting hate speech on twitter using a convolution-GRU based deep neural network. In: ESWC 2018: The Semantic Web, pp. 745–760 (2018)

  24. Abozinadah E.A., Jones J.H.: A statistical learning approach to detect abusive twitter accounts. In: Proc. Int. Conf. Comput. Data Anal.—ICCDA ’17, pp. 6–13 (2017)

  25. Haidar, B., Chamoun, M., Serhrouchni, A.: A multilingual system for cyberbullying detection: arabic content detection using machine learning. Adv. Sci. Technol. Eng. Syst. J. 2(6), 275–284 (2017)

    Article  Google Scholar 

  26. Albadi, N., Kurdi, M., Mishra, S.: Are they Our Brothers? Analysis and Detection of Religious Hate Speech in the Arabic Twittersphere. In: 2018 IEEE/ACM Int. Conf. Adv. Soc. Networks Anal. Min., pp. 69–76 (2018)

  27. Al-Hassan, A., Al-Dossari, H.: Detection of hate speech in social networks: a survey on multilingual corpus. Comput. Sci. Inf. Technol. (CS IT) 9(2), 83 (2019)

    Google Scholar 

  28. Alabbas W., Haider, M., Mansour, A., Epiphaniou, G., Frommholz, I.: Classification of Colloquial Arabic Tweets in real-time to detect high-risk floods. In: 2017 International Conference On Social Media, Wearable And Web Analytics (Social Media), pp. 1–8 (2017)

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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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  1. Information Systems Department, College of Computer Science and Information Systems, King Saud University, Riyadh, Saudi Arabia

    Areej Al-Hassan & Hmood Al-Dossari

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  1. Areej Al-Hassan
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  2. Hmood Al-Dossari
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Correspondence to Areej Al-Hassan.

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Al-Hassan, A., Al-Dossari, H. Detection of hate speech in Arabic tweets using deep learning. Multimedia Systems 28, 1963–1974 (2022). https://doi.org/10.1007/s00530-020-00742-w

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