Reconceptualizing Agenda-Setting Theory in the Age of Artificial Intelligence: An Algorithmic Agenda-Setting Framework for Media Influence, Trust, and Public Engagement

Abstract

Agenda-Setting Theory (McCombs & Shaw, 1972) established that media shape public perceptions by determining which issues are salient. This framework has guided communication research for five decades. However, the rise of Artificial Intelligence (AI), algorithmic recommendation systems, and platform-driven communication ecosystems has fundamentally restructured how information is selected, amplified, and consumed, exposing critical limitations in the theory's classical assumptions. This paper re-examines Agenda-Setting Theory in the context of AI-mediated communication and proposes the Algorithmic Agenda-Setting Framework (AASF) as a conceptual extension addressing the inadequacies of classical theory for algorithmically mediated environments. A systematic theoretical review guided by PRISMA principles was conducted, synthesizing 55 peer-reviewed studies and scholarly works (2000–2025) across communication theory, algorithmic media, platform governance, trust, and disinformation research. AI transforms agenda setting through five interconnected mechanisms: (1) algorithmic gatekeeping, (2) personalized agenda construction, (3) predictive agenda amplification, (4) trust mediation, and (5) ethical agenda distortion. These mechanisms collectively constitute the AASF, which is further contextualized through analysis of Global South media environments. The paper advances communication theory by demonstrating that algorithmic agenda setting is no longer centralized, homogeneous, or exclusively human-driven, and by introducing the AASF as a theoretically rigorous framework for AI-mediated information environments. Findings have implications for journalism practice, algorithmic accountability policy, media literacy education, democratic communication, and Global South media governance.