LINGUISTIC MARKERS OF FAKE NEWS IN ENGLISH MEDIA DISCOURSE

The article presents a comprehensive study of linguistic markers of fake news in English media discourse. The relevance of the research is determined by the rapid development of digital media, the increasing volume of disinformation, and the need to identify linguistic means that ensure manipulative influence on mass audiences. The aim of the study is to identify and systematize the linguistic markers of fake news in English media discourse, determine their lexical-semantic, grammatical, syntactic, pragmatic, and discursive features, and establish their role in creating credibility, emotional impact, and the manipulative potential of media messages. The research material consists of English fake news texts and fact-checking reports selected from the Reuters Fact Check platform. The study employs descriptive, lexical-semantic, componential, contextual, pragmalinguistic, and discourse analysis methods, as well as elements of quantitative analysis. The findings demonstrate that English fake news is characterized by the systematic use of emotionally evaluative vocabulary, modal constructions, intensifiers, generalizations, categorical statements, pseudo-authoritative references, and syntactic patterns aimed at enhancing the expressiveness and persuasiveness of media messages. The analysis also reveals the significant role of clickbait headlines, sensational vocabulary, rhetorical questions, imperative constructions, and personalization strategies in the structure of fake news. Furthermore, linguistic markers are shown to implement pragmatic strategies of intimidation, appeal to authority, conspiracy framing, sensationalism, and emotional manipulation, thereby creating an illusion of credibility. The obtained results contribute to a deeper understanding of the linguistic mechanisms underlying the functioning of English fake news and may be applied in further research in media linguistics, pragmalinguistics, critical discourse analysis, digital communication, and the development of automatic disinformation detection methods.