Stefanie Prezioso
Author directory2026
Pluralistic Annotation of Cultural Weaponization on Wikipedia: A Multi-Annotator Case Study across Ukraine and Armenia
Camil Hamdane | Hanna Perekhoda | Emanuela Boros | Stefanie Prezioso | Hamest Tamrazyan
Proceedings of the First Workshop on Pluralistic AI NLP: Diversity-aware, Sociotechnical, Responsible Alignment (PANDORA 2026)
Camil Hamdane | Hanna Perekhoda | Emanuela Boros | Stefanie Prezioso | Hamest Tamrazyan
Proceedings of the First Workshop on Pluralistic AI NLP: Diversity-aware, Sociotechnical, Responsible Alignment (PANDORA 2026)
Detecting the weaponization of cultural heritage on Wikipedia is not a straightforward classification task. Whether an edit manipulates a historical or cultural narrative often depends on context, interpretation, and the perspectives of those assessing it. We examine this challenge through a study of 400 English-language Wikipedia edits concerning Ukraine and Armenia. Three expert annotators independently classified each edit as Potentially Manipulative, Not Problematic, or Ambiguous, before resolving disagreements through structured discussion. We also compared their judgments with those of a large language model. More than one-third of the edits prompted disagreement, and discussion frequently produced decisions that could not be recovered through majority voting alone. The model aligned more closely with the experts’ deliberated decisions than with their initial majority votes, demonstrating that the construction of “gold” labels can substantially affect how model performance is understood. Our findings also show that Ambiguous captures genuine evidential and interpretive uncertainty rather than annotation failure. We argue that culturally sensitive annotation should preserve disagreement, treat ambiguity as a meaningful outcome, and use deliberation, not mechanical aggregation, to construct reference labels.