Algorithmic Gatekeeping and Democratic Communication: Who Decides What the Public Sees?

This study examines how the algorithmic structures of digital platforms such as TikTok and YouTube reshape the visibility of news content and its effects on digital journalism. The issue of which news is foregrounded or relegated to the background in the digital public sphere is not merely a matter of technical choice, but a reflection of economic, cultural, and political preferences. The theoretical analysis conducted within the framework of Habermas’s theory of the public sphere, Fraser’s counter-public sphere approach, and Mouffe’s agonistic democracy model reveals the transformations of platform capitalism and data-driven recommendation systems on democratic representation. The study concretizes the decisive role of TikTok and YouTube in the processes of news production, distribution, and consumption through three case studies. In particular, the reduction of political content to an entertainment format on TikTok and the deepening polarization of recommendation algorithms on YouTube demonstrate how the algorithmic structure contradicts journalistic values. While digital journalism is shaped according to the visibility criteria of these platforms, fundamental principles such as impartiality, diversity and access to accurate information are weakened. In conclusion, policy recommendations for algorithmic transparency, platform literacy for journalists and support for alternative media structures are developed.