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.
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.
Scholars such as Pariser (2011) introduced the concept of the “filter bubble,” while Sunstein (2017) argued for the necessity of “unplanned encounters” in a healthy public sphere. However, empirical evidence remains mixed, with some studies showing only moderate fragmentation (Bruns, 2019).
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.
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.
Scholars such as Pariser (2011) introduced the concept of the “filter bubble,” while Sunstein (2017) argued for the necessity of “unplanned encounters” in a healthy public sphere. However, empirical evidence remains mixed, with some studies showing only moderate fragmentation (Bruns, 2019).