shroukgbr at StanceNakba Shared Task: Transformer-Based Ensemble Learning for Actor-Level Stance Detection in Palestinian–Israeli Social Media Discourse

Abstract

Stance detection has become an essential task for understanding political discourse on social media, particularly in highly polarized contexts where sentiment alone is insufficient to capture author intent. This study addresses stance classification in discussions related to the Palestinian–Israeli conflict by developing transformer-based and ensemble learning approaches for three-class classification: Pr-Palestine, Pro-Israel, and Neutral. Using the StanceNakba 2026 Shared Task dataset, we fine-tune multiple pretrained transformer models, including MARBERT, ARBERT, BERT, RoBERTa, and DeBERTa, and evaluate their performance using stratified cross-validation with macro F1-score as the primary metric. In addition to individual model evaluation, a weighted ensemble combining BERT, RoBERTa, and DeBERTa is proposed to leverage complementary contextual representations. Experimental results show that the ensemble model achieves the best performance with an accuracy and macro F1-score of 0.8905, outperforming specialized Arabic models while maintaining strong class-wise balance. The proposed approach achieved first place on the Codabench leaderboard in both the development and final evaluation phases of the shared task, demonstrating its robustness and effectiveness in real-world stance detection settings.

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