Revisiting Agenda-Setting Theory in Hybrid Media Ecosystems: Flash Agendas, Agenda Leadership, and Algorithmic Curation

Traditional agenda-setting theory, which is centered on legacy news outlets, now operates in a fragmented ecosystem shaped by platforms, algorithms, and networked intermediaries. Existing agenda-setting models only partially integrate algorithmic gatekeeping and audience agency, thus limiting their capacity to explain contemporary patterns of issue salience. This study revisits classic, second-level, and network agenda-setting research. It synthesizes recent work on big data, social media, and AI-driven curation to propose an agenda-setting ecosystem conceptual model for hybrid liberal-democratic media systems that links macro-level institutions and infrastructure, meso-level networked intermediaries, and micro-level cognitive and behavioral processes. Two analytical concepts have been advanced: flash agendas, defined as rapid and short-lived spikes in public attention, and agenda leadership, defined as the capacity of specific actors to trigger, steer, and sustain such spikes across platforms. This article outlines the methodological and ethical challenges of studying these dynamics, including data access, measurement validity, and transparency of algorithmic systems. It identifies directions for empirical research and policy, with particular attention to cross-platform diffusion and feedback loops. The framework aims to support more robust theory building and measurement in hybrid, algorithmically mediated media environments.