This article applies the methods of computational folkloristics to a corpus of the Adyghe Nart epic in Russian translation. The study aims to demonstrate how quantitative procedures can formalize narrative action and the distribution of social roles. The research material is based on the 2017 edition of Narty. Adygskii geroicheskii epos, comprising approximately 100,000 word tokens. Automatically segmented into episodes, the text is processed in Python using lexical statistics, LDA topic modeling, character co-occurrence networks, event annotation, and dictionary-based motif detection. Each action yields an event record detailing the subject, object, event type, and local context. The results demonstrate a predominance of spatial, military, and economic terms, together with verbs denoting motion and combat. Network analysis confirms Sosruko as the central character and Sataney as the leading female figure within
a heavily male-dominated corpus. Also identified is the quantitative predominance of movement, military, and communicative actions. Risk-related and religious motifs are found to dominate, with family and gender themes remaining relatively underrepresented. A specific analysis of animal vocabulary reveals the functional superiority of the “horse” as a status marker and active narrative participant. The proposed reproducible event model facilitates folklore text description and allows for category refinement as new data is accumulated. Despite limitations arising from translated texts and automated annotation, this model serves as a prototype designed to enable cross-corpus comparisons and integration into broader humanities and social science research.
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