@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://aclanthology.org/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."
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%0 Conference Proceedings
%T EGCSS at StanceNakba Shared Task: Cross-Topic Arabic Stance Detection for Two Middle East Issues
%A Qindeel, Asmaa
%A Khaled, Toka
%A Najeh Balah, Batool
%A Elrefai, Eman
%A Fawzi, Mahmoud
%Y Jarrar, Mustafa
%Y El-Haj, Mo
%Y Haddad, Amal
%Y Atiani, Serin
%Y Abudalfa, Shadi
%Y Regier, Terry
%Y Rayson, Paul
%Y Sima’an, Khalil
%Y Mansour, Camille
%S Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F qindeel-etal-2026-egcss
%X 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.
%R 10.63317/3sxocvjww5ss
%U https://aclanthology.org/2026.nakbanlp-1.47/
%U https://doi.org/10.63317/3sxocvjww5ss
%P 298-302
Markdown (Informal)
[EGCSS at StanceNakba Shared Task: Cross-Topic Arabic Stance Detection for Two Middle East Issues](https://aclanthology.org/2026.nakbanlp-1.47/) (Qindeel et al., NakbaNLP 2026)
ACL