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    <abstract>Large Language Models (LLMs) are increasingly used to explain, summarize, and translate real-world events, including ongoing geopolitical conflicts. Yet it remains unclear whether they reproduce conflict-specific propaganda and, if so, how this appears in their outputs. We study this question for the Russia-Ukraine war through perspectival divergence, the extent to which model outputs align with competing narratives from different information ecosystems. We construct a conflict-aware evaluation set of neutral English event statements paired with Russian (RU)- and Ukrainian (UA)-oriented reference texts drawn from news outlets and Telegram channels. We then evaluate multiple LLMs under several prompting contexts using a reference-based semantic distance metric that measures directional proximity to RU- and UA-oriented references. To explain not only which side a model is closer to but also how that alignment is expressed, we further analyze outputs using five propaganda-relevant categories: Framing Narrative, Emotional Manipulation, Source Credibility, Social Pressure Identity, and Toponymy Naming. Across models, we find stable, model-specific leanings and technique profiles that persist across prompts and are not captured by standard factuality-oriented metrics. Our findings show that models that appear neutral under conventional evaluations can still encode systematic, conflict-specific propaganda patterns, underscoring the need for conflict-aware evaluation frameworks.</abstract>
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%0 Conference Proceedings
%T Propaganda Signals in LLMs: Perspectival Divergence and Narrative Framing in the Russia-Ukraine War
%A Shabat, Ofir
%A Guy, Ido
%A Radinsky, Kira
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F shabat-etal-2026-propaganda
%X Large Language Models (LLMs) are increasingly used to explain, summarize, and translate real-world events, including ongoing geopolitical conflicts. Yet it remains unclear whether they reproduce conflict-specific propaganda and, if so, how this appears in their outputs. We study this question for the Russia-Ukraine war through perspectival divergence, the extent to which model outputs align with competing narratives from different information ecosystems. We construct a conflict-aware evaluation set of neutral English event statements paired with Russian (RU)- and Ukrainian (UA)-oriented reference texts drawn from news outlets and Telegram channels. We then evaluate multiple LLMs under several prompting contexts using a reference-based semantic distance metric that measures directional proximity to RU- and UA-oriented references. To explain not only which side a model is closer to but also how that alignment is expressed, we further analyze outputs using five propaganda-relevant categories: Framing Narrative, Emotional Manipulation, Source Credibility, Social Pressure Identity, and Toponymy Naming. Across models, we find stable, model-specific leanings and technique profiles that persist across prompts and are not captured by standard factuality-oriented metrics. Our findings show that models that appear neutral under conventional evaluations can still encode systematic, conflict-specific propaganda patterns, underscoring the need for conflict-aware evaluation frameworks.
%U https://aclanthology.org/2026.findings-acl.557/
%P 11484-11501

Markdown (Informal)

[Propaganda Signals in LLMs: Perspectival Divergence and Narrative Framing in the Russia-Ukraine War](https://aclanthology.org/2026.findings-acl.557/) (Shabat et al., Findings 2026)

• Propaganda Signals in LLMs: Perspectival Divergence and Narrative Framing in the Russia-Ukraine War (Shabat et al., Findings 2026)

ACL

• Ofir Shabat, Ido Guy, and Kira Radinsky. 2026. Propaganda Signals in LLMs: Perspectival Divergence and Narrative Framing in the Russia-Ukraine War. In Findings of the Association for Computational Linguistics: ACL 2026, pages 11484–11501, San Diego, California, United States. Association for Computational Linguistics.