Detecting Hate Speech in Turkish Print Media: A Corpus and A Hybrid Approach with Target-oriented Linguistic Knowledge

Gökçe Uludogan, Atif Emre Yüksel, Ümit Tunçer, Burak Isik, Yasemin Korkmaz, Didar Akar, Arzucan Özgür

Abstract: The use of hate speech targeting ethnicity, nationalities, religious identities, and specific groups has been on the rise in the news media. However, most existing automatic hate speech detection models focus on identifying hate speech, often neglecting the target group-specific language that is common in news articles. To address this problem, we first compile a hate speech dataset, TurkishHatePrintCorpus, derived from Turkish news articles and annotate it specifically for the language related to the targeted group. We then introduce the HateTargetBERT model, which integrates the target-centric linguistic features extracted in this study into the BERT model, and demonstrate its effectiveness in detecting hate speech while allowing the model’s classification decision to be explained. We have made the dataset and source code publicly available at url{https://github.com/boun-tabi/HateTargetBERT-TR}.