@inproceedings{zayet-etal-2026-yafa,
    title = "Yafa at {S}tance{N}akba: Actor-Level Stance Detection Using Cross-Lingual Approach",
    author = "Zayet, Tasnim  and
      Hamed, Osama  and
      Duridi, Tasneem",
    editor = "Jarrar, Mustafa  and
      El-Haj, Mo  and
      Haddad, Amal  and
      Atiani, Serin  and
      Abudalfa, Shadi  and
      Regier, Terry  and
      Rayson, Paul  and
      Sima{'}an, Khalil  and
      Mansour, Camille",
    booktitle = "Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/cawl-year/2026.nakbanlp-1.23/",
    doi = "10.63317/4pzmawd3avoy",
    pages = "177--181",
    abstract = "This paper addresses the problem of actor-level stance detection in English social media posts concerning the Palestinian issue, a subtask of the StanceNakba-2026 Shared Task. The objective is to classify posts into one of three categories: Pro-Palestine, Pro-Israel, or Neutral, which is more challenging than the traditional favor/against/neutral formulations. This study uses a dataset comprising 1,401 posts, collected from X (formerly Twitter) after October 7, 2023, and annotated with one of the three stance labels. As Yafa{'}s Team, we tried to solve this problem using BERT-based models, which have proven their superiority in similar tasks. Several BERT-based models were fine-tuned and compared, including ARBERT, MARBERT, and PoliBERTweet, among others. Our winning model is the ``MARBERT-Y'', where the ``Y'' comes from Yafa, a MARBERT-based model that has achieved a macro-F1 score of 95{\%} on the test set. We argue this to two main factors: the structured and harsh preprocessing steps applied and the fine-tuning process employed. This indicates that domain-adapted transformer models, i.e., those pretrained on large-scale Twitter data are highly effective for politically stance detection tasks."
}