The Rashomon Wikipedia: A Data-Perspectivist Analysis of Divergent Historical Narratives

Claudiu Creanga; Liviu P. Dinu; Anca Dinu

cs.CLSep 27, 2026

The Rashomon Wikipedia: A Data-Perspectivist Analysis of Divergent Historical Narratives

Authors: Claudiu Creanga, Liviu P. Dinu, Anca Dinu

Organizations: Interdisciplinary School of Doctoral Studies, University of Bucharest, Romania · HLT Research Center, University of Bucharest, Romania · Faculty of Mathematics and Computer Science, University of Bucharest, Romania · Faculty of Foreign Languages and Literatures, University of Bucharest, Romania

Abstract

Wikipedia aims to provide a unified, neutral record of history, yet its independent language editions often function as distinct epistemic communities, creating divergent narratives around contested events. This paper investigates cross-lingual historiographical bias by analyzing Wikipedia articles across five languages (Romanian, Hungarian, Russian, Turkish, and English) focusing on three contentious events in Romanian history: the Battle of Posada (1330), the Soviet occupation of Bessarabia (1940), and the Night Attack at Targoviste (1462). Using human annotators and Large Language Models (LLMs) to classify citation stance and quantify narrative evolution from 2005 to 2024, we identify a phenomenon of "citation isolation". In the case of the Battle of Posada, only 2 out of 119 citations were shared between language editions, with the Romanian edition exhibiting a 91% pro-national bias compared to the balanced Hungarian edition. Longitudinal analysis reveals that these narratives are volatile and responsive to contemporary geopolitics, evidenced by a significant shift in the Russian framing of Bessarabia in 2024. Finally, we propose a "Peace-Maker" pipeline to automate conflict reconciliation. We demonstrate that while standard prompting leads models to hallucinate consensus, "adversarial" prompting, which explicitly instructs the model to preserve and attribute disagreement, achieves near-perfect neutrality scores.

Figures & tables

Figure 1: Raw count of citations by stance. Note the overwhelming volume of Pro-Romanian citations in the Romanian edition compared to the balanced distribution in Hungarian.

Figure 2: Stance distribution of citations in the Battle of Posada articles. Romanian Wikipedia is 91% Pro-Romanian, while Hungarian Wikipedia is balanced.

Figure 3: LLM Confidence Distribution. The high confidence scores across all stances indicate that the "Citation Isolation" is not due to model uncertainty but reflects clear, distinct narrative framing in the sources.

Panel of Figure 4 — Figure 4: Evolution of the "Pro-National" score over time. Left: Bessarabia (RO vs RU). Right: Night Attack at Târgovi s , te (RO vs TR vs EN).

Figure 5: Detailed metric comparison for Bessarabia (2024). Note the divergence in "Soviet Justification" and "Deportations".

Figure 6: Deportation mentions over time. Romanian Wikipedia consistently highlights Soviet atrocities, while Russian Wikipedia largely omits them.

Figure 7: Radar chart of narrative metrics for the Night Attack (2024). The shapes illustrate distinct narrative priorities: Turkish (Cruelty-focused), Romanian (Heroism-focused), and English (Balanced).

Figure 8: Comparison of "Vlad Cruelty" scores. Turkish Wikipedia consistently emphasizes Vlad’s brutality much more than the Romanian edition.

Figure 9: Neutrality scores (0-1) for different prompting strategies. The Adversarial prompt achieves perfect neutrality.

Figure 10: Detailed evaluation of generated summaries. The Adversarial prompt excels not just in Neutrality, but in Source Attribution and Conflict Preservation.

Prompt Strategy	Neutrality	Conflict Pres.
Standard	0.47	0.60
Mediator	0.95	1.00
Academic	0.97	1.00
Adversarial	1.00	1.00

Table 1: Performance of prompting strategies. Neutrality and Conflict Preservation are scored 0-1.

Historical Document Analysis

Historical document analysis uses AI to transcribe, interpret, organize, and retrieve information from archival texts and images.

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Abstract

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