Yafa at StanceNakba: Actor-Level Stance Detection Using Cross-Lingual Approach

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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