Abstract This paper examines the impact of algorithmic content distribution on press freedom in digital environments. It argues that press freedom is no longer determined solely by journalistic production, but is increasingly shaped by algorithmic systems that control visibility, reach, and public access to news. To address this shift, the study introduces the Algorithmic Press Freedom Framework (APFF), a conceptual model consisting of three interconnected layers: (1) the Input Layer (Journalistic Production), guided by editorial values such as accuracy, relevance, and public interest; (2) the Algorithmic Mediation Layer, where ranking, recommendation, and personalization mechanisms—driven by engagement metrics and platform policies—act as powerful intermediaries; and (3) the Output Layer (Public Access and Perception), which encompasses visibility, perceived credibility, information diversity, and audience interpretation. Drawing on a theoretical and conceptual approach, the paper analyzes how algorithmic mediation creates structural constraints for independent journalism while simultaneously offering new opportunities for reach and scalability. The findings highlight risks including algorithmic bias, reduced transparency, filter bubbles, and the potential erosion of media pluralism. The study concludes that safeguarding press freedom in the algorithmic age requires greater platform accountability, algorithmic transparency, and a renewed commitment by journalists to ethical standards and the public interest.
Gilberto Ewale Masa (Tue,) studied this question.