  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 in at least two ways: (1) a post-hoc understanding
of how engaged audiences are with the content will help production teams learn
from experience and improve future productions; (2), this type of media has
potential for real-time measures of engagement to be used to enhance the user
experience by adapting content on-the-fly. 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.
Fortuitously, the nature of interactive media facilitates a much richer set of
interaction data than traditional media; our research aims to understand if
these data can be used to infer audience engagement. In this paper, we report a
study using data captured from audience interactions with an interactive TV
show to model and predict engagement. We find that temporal metrics, including
overall time spent on the experience and the interval between events, are
predictive of engagement. The results demonstrate that interaction data can be
used to infer users' engagement during and after an experience, and the
proposed techniques are relevant to better understand audience preference and
responses.
