Decoding News Avoidance: An Immersive Dialogical Method for Inter-generational Studies

Understanding the patterns and mechanisms behind news consumption and avoidance is crucial for fostering democratic participation and informed societies. This methodological paper introduces an approach designed to study news avoidance, addressing the limitations and biases associated with traditional self-report surveys and digital-trace data collection. We propose an intelligent, dialogical news delivery application that simulates a real-world news consumption environment. This application segments content to provide nuanced interaction data while controlling for self-report response biases. Thus, the proposed method allows for the integration of behavioural and self-report data, leveraging the strengths of these divergent data types to offer a more comprehensive understanding of news engagement dynamics. By enabling controlled yet naturalistic interactions with news content, our approach seeks to unveil the multifaceted reasons behind news avoidance across different demographics, with a particular focus on understanding inter-generational dynamics. This paper underscores the importance of developing robust methodological tools in media studies to derive scientifically valid and replicable inferences that explain news consumption behaviours.