StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse

2026-06-10

AI Summary

This study addresses the challenge of stance detection in polarized social media discourse surrounding the Israeli–Palestinian conflict by proposing the first multilingual framework that jointly models actor-specific perspectives (Palestinian/Israeli) and issue-level stances on topics such as “Arab–Israeli normalization” and “Jordanian refugee presence.” The authors construct and annotate a bilingual English–Arabic dataset to enable cross-topic generalization research. Methodologically, they integrate MARBERT, AraBERT, and DeBERTa-v3 within a topic-conditioned architecture enhanced by ensemble learning strategies. Experimental results demonstrate strong performance, achieving Macro F1 scores of 0.9620 and 0.8724 on actor stance identification (Subtask A) and issue-specific stance classification (Subtask B), respectively, thereby substantiating the effectiveness and novelty of the proposed approach in conflict-sensitive contexts.

Abstract

We present StanceNakba 2026, a shared task on stance detection in polarized social media discourse related to the Palestinian-Israeli conflict, organized as part of Nakba-NLP 2026 at LREC-COLING 2026. The task introduces two subtasks: Subtask A (Actor-Level Stance Detection), which classifies English social media posts as Pro-Palestine, Pro-Israel, or Neutral; and Subtask B (Cross-Topic Stance Detection), which identifies Favor, Against, or Neither stances in Arabic posts toward two conflict-related topics, normalization with Israel and refugee presence in Jordan. The task is grounded in an annotated dataset of 2,606 social media posts. A total of 7 teams participated in Subtask A and 6 teams in Subtask B. Participating systems primarily fine-tuned Arabic and multilingual transformer-based models, including MARBERT, AraBERT, and DeBERTa-v3 variants, with several teams employing cross-validation, ensemble methods, and topic-conditioned architectures. The best-performing systems achieved a Macro F1 of 0.9620 on Subtask A and 0.8724 on Subtask B, demonstrating that transformer-based approaches are highly effective for conflict-domain stance detection while highlighting persistent challenges in cross-topic generalization and neutral class prediction.

Problem

• stance detection
• social media discourse
• Palestinian-Israeli conflict
• cross-topic generalization
• polarized discourse

Innovation

• stance detection
• cross-topic generalization
• multilingual transformers
• social media discourse
• shared task