Clickbait, Bias, and Digital Trust: A Framed Engagement Processing Model Applied to Kazan Federal University

This research examines the impact of framing styles—clickbait and bias—on user engagement and perceptions in the context of digital marketing, as exemplified through YouTube videos related to Kazan Federal University. Informed by Framing Theory and the Elaboration Likelihood Model (ELM), this study presents the Framed Engagement Processing Model (FEPM), a two-route model that explains how people respond to framing cues through peripheral or central cognitive routes. A rich dataset of 4,944 videos was analyzed through web scraping methods, sentiment analysis, and fact-checking tools. The results show that clickbait and negatively biased titles and content increase short-term user engagement; however, they can decrease credibility, especially when paired with misinformation. Balanced and fact-based framing, on the other hand, builds long-term credibility through central cognitive processing. This study suggests novel metrics—Bias Impact Score and Channel Reputation Score—for measuring credibility and user engagement. The findings emphasize the critical role of influencers, algorithms, and digital literacy in determining engagement dynamics while offering strategic implications for the ethical and effective creation of content in digital marketing. JEL Codes: M37, D83, L82 Keywords: Clickbait, Biased Content, Kazan Federal University (KFU), Framing Theory, Elaboration Likelihood Model, FEPM.