Authors - Dzhansel Abtula, Stanka Hadzhikoleva, Emil Hadzhikolev, Iliana Ivanova, Elitsa Dubarova Abstract - The present study examines the potential of generative language models as tools for discourse analysis of war-related language in media texts. The study is based on a corpus of texts processed by three generative AI models using an identical prompt that defines a multi-stage analytical procedure. This procedure includes the extraction of war-related lexical units, semantic classification, functional analysis of evaluative and ideological features, compilation of a thematic glossary, analysis of metaphors, identification of discursive strategies, and genre determination of the texts. The generated analyses are evaluated through a structured questionnaire based on a five-point Likert scale, completed by an expert with an academic background. The study aims to systematically assess and compare the quality of discourse analyses produced by different language models under controlled conditions. The results indicate that all models generate structurally coherent and terminologically consistent analyses, but differ significantly in interpretative depth and contextual sensitivity. These findings support the need for a hybrid approach that combines automated analysis with human expertise to ensure accurate interpretation of implicit meanings, ideological nuances, and context-dependent discourse features.
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