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

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.

Computation and Language (cs.CL)

arXiv:2606.12068 [cs.CL]

(or arXiv:2606.12068v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2606.12068 arXiv-issued DOI via DataCite (pending registration)

Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources (Nakba-NLP 2026), LREC-COLING 2026, pp. 80-90, ELRA Language Resources Association, 2026

From: Md.Rafiul Biswas Mr. [view email] [v1] Wed, 10 Jun 2026 13:32:10 UTC (47 KB)