CORPUS AND CONTENT ANALYSIS OF SUGGESTION IN DIGITAL PROPAGANDA ON SOCIAL MEDIA

Abstract

This study examines suggestion as a latent mechanism of influence in digital propaganda, particularly in bilingual Ukrainian- and English-language social media discourse related to the Russian Ukrainian war, civil protection, security, and crisis communication. The pilot material consists of publicly available posts and comments from Telegram channels, X (Twitter), TikTok, and YouTube, selected for testing a combined corpus-and-content analysis procedure. The study argues that suggestion operates through repetition, emotionally marked vocabulary, metaphorical and archetypal framing, binary oppositions, threat narratives, keyword salience, and network amplification. The methodological framework combines corpus analysis in Sketch Engine, content and network analysis in Communalytic, and an author developed Digital Suggestion Index (DSI) based on weighted feature extraction. The proposed approach supports the preliminary identification of manipulative messages, coordinated dissemination, recurrent propaganda frames, and suggestive linguistic patterns.