StanceNakba 2026 Shared Task: Stance Detection in Palestinian-Israeli Conflict Discourse
Researchers introduced StanceNakba 2026, a shared NLP task challenging teams to detect stances in social media posts about the Palestinian-Israeli conflict across English and Arabic. The task used a dataset of 2,606 annotated posts and attracted 7 and 6 teams for its two subtasks, respectively. The results advance automated tools for analyzing polarized political discourse, though challenges remain in cross-topic generalization and identifying neutral positions.
StanceNakba 2026 is a shared task organized as part of the Nakba-NLP 2026 workshop at LREC-COLING 2026, focused on stance detection in polarized social media discourse related to the Palestinian-Israeli conflict. Subtask A required classifying English posts as Pro-Palestine, Pro-Israel, or Neutral, while Subtask B involved identifying Favor, Against, or Neither stances in Arabic posts toward two specific topics: normalization with Israel and refugee presence in Jordan. The task was grounded in an annotated dataset of 2,606 social media posts. Participating teams primarily fine-tuned transformer-based models such as MARBERT, AraBERT, and DeBERTa-v3, with several employing ensemble methods and topic-conditioned architectures. The best systems achieved Macro F1 scores of 0.9620 on Subtask A and 0.8724 on Subtask B, indicating strong overall performance. However, the paper notes persistent difficulties in cross-topic generalization and neutral class prediction, pointing to open challenges for future work in conflict-domain NLP.
What's missing
The paper does not detail the sources or platforms from which the 2,606 social media posts were collected, the annotation methodology or inter-annotator agreement scores, or how the dataset handles potential label imbalance between Pro-Palestine, Pro-Israel, and Neutral classes. Ethical considerations around deploying such stance detection systems in real-world conflict contexts are also not addressed in the abstract.
What different sources said
- arXiv cs.CLCenter
StanceNakba Shared Task: Actor and Topic-Aware Stance Detection in Public Discourse
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