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Симметрийные понятия в актуальном медиатексте: способы создания новых лексических значений

Authors: Ponomarenko, I.N.; Kim, D.A.; Segal, N.A.;

Симметрийные понятия в актуальном медиатексте: способы создания новых лексических значений

Abstract

Пономаренко Ирина Николаевна, доктор филологических наук, доцент, профессор кафедры современного русского языка, Кубанский государственный университет (Краснодар), irnik20@mail.ru Ким Дмитрий Анатольевич, ассистент кафедры современного русского языка, Кубанский государственный университет (Краснодар), dmtk-b@mail.ru Сегал Наталья Александровна, кандидат филологических наук, доцент, доцент кафедры русского, славянского и общего языкознания, Крымский федеральный университет им. В.И. Вернадского (Симферополь), natasha-segal@mail.ru Irina N. Ponomarenko, Doctor of Philology, associate professor, professor of the Department of Modern Russian Language, Kuban State University (Krasnodar), irnik20@mail.ru Dmitrij A. Kim, assistant of the Department of Modern Russian Language, Kuban State University (Krasnodar), dmtk-b@mail.ru Natalya A. Segal, Candidate of Philology, associate professor, associate professor of the Department of Russian, Slavic and General Linguistics, V.I. Vernadsky Crimean Federal University (Simferopol), natashasegal@mail.ru) Предлагаемая статья посвящена комплексному анализу симметрийных понятий добро и зло в структуре единиц медийного лексикона как инструмента усиления аксиологического модуса интерпретации актуальных социополитических событий. Доказывается, что именно симметрийные понятия создают двуполярность медиатекстов и вариативность интерпретации определенных политических событий. Анализ медиатекстов приводит к выводу, что ценностный модус симметрийной категории добро / зло в медиатекстах не является закрепленным, что приводит к дискуссионности суждений, невозможности определить, злом или благом является та или иная экономическая, социальная или политическая ситуация. Такая особая аксиологичность медиасреды меняет структурные координаты исследуемых лексем в языковой системе, трансформирует их семантическое значение и отражает многополярность и многовекторность современной политической коммуникации.This article is devoted to a comprehensive analysis of the symmetrical concepts of good and evil in the structure of units of the media lexicon as a tool to strengthen the axiological mode of interpretation of current sociopolitical events. It is proved that it is precisely the symmetrical concepts that create the bipolarity of media texts and variability of the interpretation of certain political events. The analysis of media texts leads to the conclusion that the value mode of the symmetrical category good / evil in media texts is not fixed, which leads to discussion of opinions, inability to decide whether this or that economic, social or political situation is evil or good. This particular axiological nature of the media environment changes the structural coordinates of the studied lexemes in the language system and transforms their semantics.

Country
Russian Federation
Keywords

media text, axiological opposition, медиатекст, good, зло, media environment, добро, симметрийная категория, медиасреда, аксиологическая оппозиция, symmetrical category, evil, 81.16 [УДК 801.3]

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green