@inproceedings{seker-etal-2025-hatecat,
    title = "{HATECAT}-{TR}: A Hate Speech Span Detection and Categorization Dataset for {T}urkish",
    author = {{\c{S}}eker, Hasan Kerem  and
      Uludo{\u{g}}an, G{\"o}k{\c{c}}e  and
      {\"O}nal, Pelin  and
      {\"O}zg{\"u}r, Arzucan},
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.1393/",
    doi = "10.18653/v1/2025.findings-emnlp.1393",
    pages = "25568--25579",
    ISBN = "979-8-89176-335-7",
    abstract = "Hate speech on social media in Turkey remains a critical issue, frequently targeting minority groups. Effective moderation requires not only detecting hateful posts but also identifying the specific hateful expressions within them. To address this, we introduce HATECAT-TR, a span-annotated dataset of Turkish tweets, containing 4465 hateful spans across 2981 posts, each directed at one of eight minority groups. Annotations were created using a semi-automated approach, combining GPT-4o-generated spans with human expert review to ensure accuracy. Each hateful span is categorized into one of five discourse types, enabling a fine-grained analysis of the nature and intent behind hateful content. We frame span detection as binary and multi-class token classification tasks and utilize the state-of-the-art language models to establish a baseline performance for the new dataset. Our findings highlight the challenges of detecting and categorizing implicit hate speech, particularly when spans are subtle and highly contextual. The source code is available at github.com/boun-tabi/hatecat-tr and HATECAT-TR can be shared by complying with the terms of X upon contacting the authors."
}