Actor and Topic-Aware Stance Detection in Public Discourse

The StanceNakba 2026 Shared Task addresses stance detection in polarized social media discourse on the Palestinian-Israeli conflict and related regional issues. This task introduces a dual-framework approach that distinguishes between actor-level political alignments and cross-topic stance patterns across two conflict-related subjects.

Participants will develop models to analyze social media posts across two subtasks: Subtask A (Actor-Level Stance Detection) identifies whether authors express Pro-Palestine, Pro-Israel, or Neutral orientations in their general position toward the Palestinian-Israeli conflict, while Subtask B (Cross-Topic Stance Detection) detects Favor, Against, or Neither stances toward specific conflict-related topics: normalization with Israel and refugee presence in Jordan.

This dual-framework enables investigation of fundamental questions: How do general political alignments (actors) relate to positions on specific issues (targets)? Can models learn generalizable stance patterns that transfer across different topics?

Models will be evaluated using multiple metrics to ensure comprehensive assessment. Each subtask will have a separate leaderboard.

This shared task will attract participants from stance detection, Arabic NLP, and computational social science communities by offering well-curated datasets and clear evaluation metrics for two distinct challenges.

Researchers can participate in either English actor-level stance detection (accessible to the general NLP community) or Arabic cross-topic stance detection (for Arabic NLP specialists), with both subtasks addressing real-world applications in understanding polarized discourse and conflict narratives on social media.