Identifying Universal Precedent Phenomena in Media Texts Using Python

2024

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.30564/fls.v6i6.7318
Data confidence 70 medium
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

This article explores the role of precedent phenomena in media texts. The study's primary goal and innovation lie in analyzing how universal precedent phenomena – such as Stockholm Syndrome, Achilles' Heel – are used in contemporary Kazakh, Russian, and English media. A key focus is on how these phenomena are perceived by students. To conduct the research, we utilized materials from mass media and online sources from Kazakhstan, Russia, and the USA. We employed contextual analysis, generalization, and comparative and descriptive methods to systematize the data. Google Forms served as a research tool, and a custom Python program was developed for linguistic analysis and to create a comprehensive database of precedent phenomena across these three languages. The study revealed how well-known concepts like “Stockholm Syndrome”, "Achilles' Heel” are represented in media texts across the three languages. It also assessed students' recognition and acceptance of these phenomena. Additionally, we developed a detailed characterization of 55 universal and national precedent phenomena in English, Russian, and Kazakh. Examples include "Romeo and Juliet”, "Red Rose”, "Phoenix”, "White Rabbit”, "Pandora's Box”, "Baba Yaga”, "Korkyt Ata”, and "Asan Kaigy”. This information was integrated into a Python program that automatically identifies these phenomena within texts and provides descriptions in all three languages. The database will continue to grow, aiding in the identification of precedent phenomena in media content and helping readers quickly understand their specific usage.