Using Data to Understand How Audiences Engage with Interactive Media

  • Jonathan Carlton

Student thesis: Phd


Media is evolving from traditional linear narratives to personalised experiences, where control over information (or how it is presented) is given to individual audience members. Measuring and understanding audience engagement with this media is important: a post-hoc understanding of how engaged audiences are with the content will help production teams learn from experiences and improve future productions. Engagement is typically measured by asking samples of users to self-report, which is time consuming and expensive. In some domains, however, interaction data have been used to infer engagement. The nature of interactive media facilitates a much richer set of interaction data than traditional media; this thesis aims to understand if these data can be used to understand and infer audience engagement and, by extension, the abandonment of content. This thesis reports studies, run in collaboration with the BBC, of engagement and abandonment using data captured from audience interactions with an interactive TV show and an adaptive tutorial. It was found that engagement can be modelled and predicted in the interactive TV show, and that users appear to behave differently based on their level of engagement. For example, high engagement is associated with consumption-type behaviours, while low engagement is associated with skipping-type behaviours. When investigating the data collected from the adaptive tutorial, the results revealed that user context, rather than user interactions, affects the engagement of users. Abandonment was investigated using a wider dataset collected from the national release of the interactive TV show; it was demonstrated that abandonment could be accurately predicted from the interactions of users. An increase in moving backwards and forwards in the show were indicative of an increase in abandonment, suggesting an exploratory-type behaviour. When exploring the link between abandonment and engagement, it was found that low engagement users were predicted to drop out further from the end, suggesting a relationship between the two. The results demonstrate that interaction data is a viable method for the evaluation of media is this evolving domain. To move towards consistency in the interaction data analysis field, the thesis proposes a framework to provide methodological support for researchers. Through an analysis of the literature, meta-issues were identified in the communication of research which create barriers in reproducibility and reduces transparency. The framework provides structure for those undertaking research on understanding users through their interactions and a terminology that can be applied consistently across the different disciplines in this area. It is conjectured that using such a framework should improve both the quality of science and science communication in the area, with more reproducible and transparent research being enabled.
Date of Award31 Dec 2022
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorJohn Keane (Supervisor) & Caroline Jay (Supervisor)


  • user modelling
  • interactive media
  • abandonment
  • interaction data
  • engagement

Cite this