@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T PoliStance-TR: A Dataset for Turkish Stance Detection in Political Domain
%A Unal, Muhammed Cihat
%A Sarkın, Yasemin
%A Karamanlioglu, Alper
%A Demirel, Berkan
%Y Angelova, Galia
%Y Kunilovskaya, Maria
%Y Escribe, Marie
%Y Mitkov, Ruslan
%S Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F unal-etal-2025-polistance
%X 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.
%U https://aclanthology.org/2025.ranlp-1.148/
%P 1282-1288
Markdown (Informal)
[PoliStance-TR: A Dataset for Turkish Stance Detection in Political Domain](https://aclanthology.org/2025.ranlp-1.148/) (Unal et al., RANLP 2025)
ACL
- Muhammed Cihat Unal, Yasemin Sarkın, Alper Karamanlioglu, and Berkan Demirel. 2025. PoliStance-TR: A Dataset for Turkish Stance Detection in Political Domain. In Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era, pages 1282–1288, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.