arXiv:2606.12068v1 Announce Type: new 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 p
The proliferation of polarized online discourse and advancements in NLP techniques are driving the need for sophisticated stance detection in conflict-related contexts.
Accurate stance detection in highly polarized social media is crucial for understanding public opinion, identifying disinformation, and potentially influencing diplomatic efforts or conflict resolution.
The development of actor and topic-aware stance detection models will enable more nuanced analysis of public sentiment and potentially shape how information warfare is conducted or countered.
- · Governments
- · Intelligence agencies
- · Social media analytics firms
- · Researchers in computational social science
- · Actors relying on covert or ambiguous online influence campaigns
- · Platforms struggling with content moderation
Improved automated tools for identifying and categorizing online sentiment regarding political conflicts.
Enhanced capabilities for strategic communication and counter-disinformation efforts by state and non-state actors.
Potential for AI to be weaponized in information warfare through advanced targeting and manipulation based on detected stances.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL