Eye-tracking Experiment of Agenda Setting Function on TouTiao APP

Abstract

Building upon the profound influence of traditional media on public agendas through agenda setting, this study focuses on how the widespread penetration of AI technology reshapes the landscape of news dissemination within the rapidly evolving media ecosystem. Specifically, with the emergence of novel news media platforms like China's Toutiao APP, these platforms, armed with data-driven content distribution mechanisms, challenge the traditional sender-centric information transmission model. Against this backdrop, this research aims to explore whether agenda setting retains its effectiveness and influence in the era of AI-driven news. To validate this inquiry, this study adopts a comprehensive empirical research methodology, integrating eye-tracking technology with questionnaire surveys, to delve into audience reading behavior patterns under the influence of AI recommendation algorithms. Specifically, by selecting China's Toutiao APP as the experimental material, this study manipulates the order of news items displayed on the interface (top versus bottom), meticulously observing and recording participants' reading attention allocation, comprehension depth, and memory retention, thereby scientifically assessing the effectiveness and underlying mechanisms of agenda setting in an AI-news environment. This research not only enriches the application boundaries of agenda setting theory in the digital age but also provides empirical evidence for understanding the profound impact of AI technology in the field of news dissemination. It holds significant implications for guiding future news media strategy formulation, optimizing user experience, and promoting the healthy development of the media ecosystem.