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.

翻译：我们提出StanceNakba 2026共享任务，这是一项在巴以冲突相关的极化社交媒体话语中开展立场检测的联合任务，作为LREC-COLING 2026大会中Nakba-NLP 2026研讨会的一部分。该任务引入两个子任务：子任务A（行动者级立场检测）将英文社交媒体帖子分类为亲巴勒斯坦、亲以色列或中立；子任务B（跨话题立场检测）识别阿拉伯语帖子对两个冲突相关话题（与以色列正常化及约旦难民存在）所持的赞成、反对或中立立场。该任务基于一个包含2,606条社交媒体帖子的已标注数据集。共有7个团队参与子任务A，6个团队参与子任务B。参与系统主要微调了阿拉伯语及多语言Transformer模型，包括MARBERT、AraBERT及DeBERTa-v3变体，多个团队采用交叉验证、集成方法和话题条件架构。最佳系统在子任务A上达到Macro F1值0.9620，在子任务B上达到0.8724，表明基于Transformer的方法对冲突领域立场检测具有高效性，同时凸显了跨话题泛化及中立类预测方面存在的持续挑战。