@inproceedings{ozturk-etal-2025-make,
    title = "Make Satire Boring Again: Reducing Stylistic Bias of Satirical Corpus by Utilizing Generative {LLM}s",
    author = "Ozturk, Asli Umay  and
      Cekinel, Recep Firat  and
      Karagoz, Pinar",
    editor = "Sharoff, Serge  and
      Terryn, Ayla Rigouts  and
      Zweigenbaum, Pierre  and
      Rapp, Reinhard",
    booktitle = "Proceedings of the 18th Workshop on Building and Using Comparable Corpora (BUCC)",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest_wac_2008/2025.bucc-1.4/",
    pages = "19--35",
    abstract = "Satire detection is essential for accurately extracting opinions from textual data and combating misinformation online. However, the lack of diverse corpora for satire leads to the problem of stylistic bias which impacts the models' detection performances. This study proposes a debiasing approach for satire detection, focusing on reducing biases in training data by utilizing generative large language models. The approach is evaluated in both cross-domain (irony detection) and cross-lingual (English) settings. Results show that the debiasing method enhances the robustness and generalizability of the models for satire and irony detection tasks in Turkish and English. However, its impact on causal language models, such as Llama-3.1, is limited. Additionally, this work curates and presents the Turkish Satirical News Dataset with detailed human annotations, with case studies on classification, debiasing, and explainability."
}