Skip to main content

Hate Speech Detection in Social Media for the Kurdish Language

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1431)


With the rapid growth of technology over the world, especially, on the internet, people enormously use social media freely to express their ideologies. Sometimes the freedom of media is caught up and the rights of others are beaten down by hate speech. Moreover, social media is an easy and vague way to desecrate people, groups, and parties since there is no any way to recognize anonymous users over social media. Testing human speech is common for English, Arabic, and Turkish languages while there is no attempt for the Kurdish language. For that reason, the Kurdish hate speech dataset is collected from comments on the Facebook application as an effort for detecting hate speech and removing them. The raw dataset consists of 6882 comments which are divided into hate and hot hate classes. Support Vector Machine (SVM), Decision Tree (DT), and Naïve Bays (NB) algorithms are implemented and compared. Based on the results, the SVM is found most excellent with the F1 measure being 0.687.


  • Kurdish hate speech detection
  • Machine learning algorithms
  • Text classification

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions


  1. Al-Hassan, A., Al-Dossari, H.: Detection of hate speech in social networks: a survey on multilingual corpus. In: 6th International Conference on Computer Science and Information Technology, vol. 10 (2019)

    Google Scholar 

  2. Saeed, A.M., Rashid, T.A., Mustafa, A.M., Agha, R.-R., Shamsaldin, A.S., Al-Salihi, N.K.: An evaluation of Reber stemmer with longest match stemmer technique in Kurdish Sorani text classification. Iran J. Comput. Sci. 1(2), 99–107 (2018).

    CrossRef  Google Scholar 

  3. Alotaibi, A., Hasanat, M.H.A.: Racism detection in twitter using deep learning and text mining techniques for the Arabic language. In: 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH), pp. 161–164. IEEE (2020)

    Google Scholar 

  4. Rashid, T.A., Mustafa, A.M., Saeed, A.R.: Automatic Kurdish text classification using KDC 4007 dataset. In: Barolli, L., Zhang, M., Wang, X. (eds.) Advances in Internetworking, Data and Web Technologies, vol. 6, pp. 187–198. Springer, Cham (2017).

    CrossRef  Google Scholar 

  5. Istaiteh, O., Al-Omoush, R., Tedmori, S.: Racist and sexist hate speech detection: literature review. In: 2020 International Conference on Intelligent Data Science Technologies and Applications (IDSTA), pp. 95–99. IEEE (2020)

    Google Scholar 

  6. Alfina, I., Mulia, R., Fanany, M.I., Ekanata, Y: Hate speech detection in the Indonesian language: a dataset and preliminary study. In: 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS), pp. 233–238. IEEE (2017)

    Google Scholar 

  7. Del Vigna12, F., Cimino23, A., Dell’Orletta, F., Petrocchi, M., Tesconi, M.: Hate me, hate me not: hate speech detection on Facebook. In: Proceedings of the First Italian Conference on Cybersecurity (ITASEC17), pp. 86–95 (2017)

    Google Scholar 

  8. Nazir, M.U., et al.: Social media competitive analysis - a case study in the pizza industry of Pakistan. In: Bajwa, I., Kamareddine, F., Costa, A. (eds.) Intelligent Technologies and Applications, vol. 932, pp. 313–325. Springer, Singapore (2019).

    CrossRef  Google Scholar 

  9. Rashid, T.A., Mustafa, A.M., Saeed, A.: A robust categorization system for Kurdish Sorani text documents. Inf. Technol. J. 16(1), 27–34 (2017)

    CrossRef  Google Scholar 

  10. Sohrabi, M.K., Hemmatian, F.: An efficient preprocessing method for supervised sentiment analysis by converting sentences to numerical vectors: a twitter case study. Multimedia Tools Appl. 78(17), 24863–24882 (2019).

    CrossRef  Google Scholar 

  11. Hegazi, M.O., Al-Dossari, Y., Al-Yahy, A., Al-Sumari, A., Hilal, A.: Preprocessing Arabic text on social media. Heliyon 7(2), e06191 (2021)

    Google Scholar 

  12. Mullah, N.S., Zainon, W.M.N.W.: Advances in machine learning algorithms for hate speech detection in social media: a review. IEEE Access 9, 88364–88376 (2021).

    CrossRef  Google Scholar 

  13. Huang, Y., Wang, R., Huang, B., Wei, B., Zheng, S.L., Chen, M.: Sentiment classification of crowdsourcing participants’ reviews text based on LDA topic model. IEEE Access 9, 108131–108143 (2021)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Ari M. Saeed .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Saeed, A.M., Ismael, A.N., Rasul, D.L., Majeed, R.S., Rashid, T.A. (2022). Hate Speech Detection in Social Media for the Kurdish Language. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the ICR’22 International Conference on Innovations in Computing Research. ICR 2022. Advances in Intelligent Systems and Computing, vol 1431. Springer, Cham.

Download citation