FEATURES OF USING LEXICAL TRANSLATION TRANSFORMATIONS FOR TRANSLATING POLITICAL NEWS TEXTS (BASED ON BBC NEWS MATERIALS)

Abstract

The article examines the specifics of applying lexical translation transformations in the process of translating political news from English into Ukrainian based on BBC texts. Revolutionary changes in the field of mass communication at the beginning of the 21st century are analysed, particularly the impact of the development of internet technologies and social networks on the formation of new requirements for news texts, characterized by promptness, accessibility, and relevance. An overview of scientific research on lexical translation transformations of media texts for the period 2000–2024 is presented, demonstrating the evolution from the establishment of media linguistics as a separate field to modern interdisciplinary approaches considering media digitalization and the influence of artificial intelligence.The main focus is on a detailed analysis of seven types of lexical transformations: addition, omission, substitution, generalization, compensation, transliteration, and antonymy. Each type of transformation is illustrated with specific examples from original BBC texts and their Ukrainian translations, allowing to trace patterns and specifics of the translation process.The research shows that the choice of a specific transformation is determined by the need to overcome the absence of direct dictionary correspondences, differences in the semantic structure of words in different languages, cultural features, and stylistic requirements of the target text. Special attention is paid to politically marked transformations that reflect the specifics of the Ukrainian context of perceiving international events.The research results demonstrate that lexical translation transformations are a key tool for ensuring adequacy and equivalence in translating political news. They allow adapting the text to linguistic norms, cultural realities, and informational needs of the target audience while maintaining factual accuracy and stylistic correspondence to the news text genre.