聚焦巴以冲突的立场检测，区分支持方与中立态度。
StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse
- 基于双任务设计，分别识别英阿语社交媒体中的立场倾向。
- 最佳模型在英文任务上达0.9620的宏平均F1，阿拉伯语任务为0.8724。
- 适合关注地缘政治舆情分析与多语言情感计算的研究者。
我们介绍StanceNakba 2026，这是在LREC-COLING 2026期间举办的关于巴以冲突相关社交媒体舆论立场检测的共享任务，属于Nakba-NLP 2026系列活动。该任务包含两个子任务：子任务A（角色级立场检测）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）则识别阿拉伯语帖子对“与以色列正常化”和“约旦难民存在”两个议题的赞成、反对或无明确立场。任务基于包含2,606条社交媒体帖子的标注数据集，共有7支团队参与子任务A，6支团队参与子任务B。参赛系统主要微调了阿拉伯语及多语言Transformer模型，包括MARBERT、AraBERT和DeBERTa-v3变体，并采用交叉验证、集成方法及话题条件化架构。最佳系统在子任务A上取得0.9620的宏平均F1，在子任务B上为0.8724，表明基于Transformer的方法在冲突领域立场检测中极为有效，同时凸显了跨话题泛化与中立类预测的持续挑战。
原文摘要 · Abstract (English)
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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