@inproceedings{unal-etal-2025-polistance,
    title = "{P}oli{S}tance-{TR}: A Dataset for {T}urkish Stance Detection in Political Domain",
    author = "Unal, Muhammed Cihat  and
      Sark{\i}n, Yasemin  and
      Karamanlioglu, Alper  and
      Demirel, Berkan",
    editor = "Angelova, Galia  and
      Kunilovskaya, Maria  and
      Escribe, Marie  and
      Mitkov, Ruslan",
    booktitle = "Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, Bulgaria",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.ranlp-1.148/",
    pages = "1282--1288",
    abstract = "Stance detection in NLP involves determining whether an author is supportive, against, or neutral towards a particular target. This task is particularly challenging for Turkish due to the limited availability of data, which hinders progress in the field. To address this issue, we introduce a novel dataset focused on stance detection in Turkish, specifically within the political domain. This dataset was collected from X (formerly Twitter) and annotated by three human annotators who followed predefined guidelines to ensure consistent labeling and generalizability. After compiling the dataset, we trained various transformer-based models with different architectures, showing that the dataset is effective for stance classification. These models achieved an impressive Macro F1 score of up to 82{\%}, highlighting their effectiveness in stance detection."
}