This paper examines whether adapting a multilingual large language model (LLM) to clearly pro‑Kremlin or pro‑Western texts can improve the automatic detection of pro‑Kremlin narratives in Czech‑language content. The study compares three variants of the same model: a base multilingual model, a pro‑Kremlin adapter trained on Russian‑language texts with pro‑Kremlin framing, and a pro‑Western adapter trained on Western sources that respond to the same topics from an opposing perspective. All models are fine‑tuned using a parameter‑efficient method and evaluated on a small Czech corpus covering five key narrative types. The pro‑Kremlin adapter shows the strongest ability to distinguish between texts with pro‑Kremlin framing and neutral texts, while the pro‑Western adapter brings only a modest improvement over the base model. These findings suggest that exposing an LLM to ideologically aligned training data can make it more sensitive to that ideology, with potential applications for supporting the detection of hostile information operations in smaller‑language NATO member states.