Mack E Media › ❲FRESH❳

Mack E. Media Department of Communication Studies University of Media Arts

This paper examines how algorithm-driven media platforms fragment public discourse into isolated ideological enclaves. Using a mixed-methods approach — content analysis of 5,000 social media posts and semi-structured interviews with 30 users — the study finds that personalization algorithms significantly reduce cross-ideological exposure. Results indicate a 62% decrease in diverse content encounters compared to non-personalized feeds. The paper concludes with recommendations for interface transparency and user agency. mack e media

Contemporary media environments are increasingly shaped by machine learning algorithms that prioritize engagement. While personalization enhances user experience, concerns have grown over its impact on democratic deliberation. This study investigates the hypothesis that algorithmic curation fragments public discourse. Mack E