Abstract
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.
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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. https://doi.org/10.1007/978-3-031-14054-9_24
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