АЛГОРИТАМСКА ИНТЕРТЕКСТУАЛНОСТ ЗА КЛАСИФИКАЦИЈА НА ВАРИЈАНТИ НА НАРОДНИ ПЕСНИ И ПРОНАОЃАЊЕ СПОДЕЛЕНИ ТЕКСТУАЛНИ ФОРМУЛИ. - Спироски, Огнен (Macedonian Folklore / Makedonski Folklor, 2025)
This paper presents the successful use of algorithms for finding the same source code (text) in a collection of program codes (software plagiarism), applied for recognizing sections of the same text in Macedonian folk songs, and for clustering by similarity, finding and classification of textual song variants. This is the first example in the literature of algorithmic intertextuality in oral poetry, folklore and ethnomusicology for the study of song variants, using methods for detecting software plagiarism. The paper presents a brief review of the literature and research related to intertextuality and variants in folk songs, which has given rise to new classification models and theories in science, as well as a large number of publications with song variants. Algorithmic intertextuality enables new digital analyses of large corpora of songs, variants, and textual formulas. Two corpora of songs are analyzed with the software tool JPlag v5.1. The first corpus contains 51 textual variants from 6 song families that have already been studied, and the algorithms cluster them according to their known song families. The second corpus is a large but uncurated collection of 1.467 song texts from the website www.pesna.org. The algorithms find and group song variants, as well as a multitude of shared textual formulas between songs, that offer interesting insights into the intertextuality and formulaic nature of folk poetry and its evolution.