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

• URL: http://arxiv.org/abs/2606.12068v1
• Date: Wed, 10 Jun 2026 13:32:10 GMT
• Title: StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse
• Abstract summary: StanceNakba 2026 is a shared task on stance detection in polarized social media discourse related to the Palestinian-Israeli conflict.<n>Subtask A (Actor-Level Stance Detection) classifies English social media posts as Pro-Palestine, Pro-Israel, or Neutral.<n>Subtask B (Cross-Topic Stance Detection) identifies Favor, Against, or Neither stances in Arabic posts toward two conflict-related topics.
• Score: 0.6742937853471207
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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