An AI-Driven News Impact Monitoring Framework Through Attention Tracking

The paper presents the motivation, development, and evaluation of an AI-driven framework for media stream impact analysis at the consumption end, employing user reactions monitoring through attention tracking (i.e., eye and mouse tracking). The adopted methodology elaborates on software and system engineering processes, combining elements of rapid prototyping models with interdisciplinary participatory design and evaluation, leaning on the foundation of information systems design science research to enable continuous refinement through repeated cycles of stakeholder engagement, feedback, technical iteration, and validation. A dynamic Form Builder has been implemented to supplement these tools, allowing the construction and management of pre- and post-intervention questionnaires, thus helping associate collected data with the respective tracking maps. The present begins with the detailed presentation of the tools’ implementation, the respective technology, and the offered functionalities, emphasizing the perception of tampered visual content that is used as a pilot evaluation and validation case. The significance of the research lies in the practical applications of AI-assisted monitoring to effectively analyze and understand media dynamics and user reactions. The so-called iMedius framework introduces an integration of innovative multidisciplinary procedures that bring together research instruments from the social sciences and multimodal analysis tools from the digital world.