@inproceedings{qindeel-etal-2026-egcss,
    title = "{EGCSS} at {S}tance{N}akba Shared Task: Cross-Topic {A}rabic Stance Detection for Two {M}iddle {E}ast Issues",
    author = "Qindeel, Asmaa  and
      Khaled, Toka  and
      Najeh Balah, Batool  and
      Elrefai, Eman  and
      Fawzi, Mahmoud",
    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/ingest-nlpsi/2026.nakbanlp-1.47/",
    doi = "10.63317/3sxocvjww5ss",
    pages = "298--302",
    abstract = "Stance detection continues to be an important task sitting at the intersection of Natural Language Processing (NLP) and Computational Social Science (CSS). In this work, we evaluate how different variations of BERT models perform on the cross-topic form of the task. In particular, we inspect their performance on the second subtask of the shared task StanceNakba 2026, where two topics are included, namely Arab Normalization with Israel and The Presence of Refugees in Arab Countries. We find that the best-performing model was bert-base-arabertv02-twitter, and we further improve its performance by providing context about the topic during the training phase, achieving an F1-score of 0.86 and ranking second among the participating teams."
}