Television shows ideation, and testing with smart digital twins to advance ratings

Abstract

Generating ideas for immersive television shows is fundamental to the television industry. TV channel managers are looking to stay ahead of their competitors and are turning to many advanced technologies like artificial intelligence (AI), the Internet of Things, virtual reality, cloud and fog computing. These technologies with other autonomous devices, technologies, surveys, models, and software are creating extensive, complex, and diverse television data sets. These data diversity and heterogeneity may hinder television research. Thus, there is a clear need to synthesize, synchronize, and integrate the large-scale data sets according to predefined decision rules and research objectives. Against this backdrop, this paper introduces a new platform of data integration and modeling—television digital twins. Digital twins (DTs) are virtual copies of products, services, processes, or humans encompassing all the relevant entities’ qualities. Although numerous research studies have been published on DTs, none hitherto have been conducted in media and television. This research aims to bridge two perspectives: on one side, the authors acknowledge the value of TVDT as a data fusion platform. On the other side, the authors build on previous scholarship to suggest a conceptual framework for implementing this platform in future TV studies.