Mack E Media May 2026

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

Findings support the fragmentation thesis but suggest that user behavior (e.g., selective liking) interacts with algorithms. Implications for media literacy and regulatory design are considered. Mack E