@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Yafa at StanceNakba: Actor-Level Stance Detection Using Cross-Lingual Approach
%A Zayet, Tasnim
%A Hamed, Osama
%A Duridi, Tasneem
%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 zayet-etal-2026-yafa
%X 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.
%R 10.63317/4pzmawd3avoy
%U https://aclanthology.org/2026.nakbanlp-1.23/
%U https://doi.org/10.63317/4pzmawd3avoy
%P 177-181
Markdown (Informal)
[Yafa at StanceNakba: Actor-Level Stance Detection Using Cross-Lingual Approach](https://aclanthology.org/2026.nakbanlp-1.23/) (Zayet et al., NakbaNLP 2026)
ACL