Mack E Media May 2026

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.

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.

Algorithmic media ecosystems risk undermining the conditions for shared public discourse. Future research should explore intervention designs that preserve personalization while ensuring minimum diversity thresholds.