Amaya Herranz Donnan
ABSTRACT
Accurately quantifying audience appreciation poses significant technical challenges, privacy concerns and difficulties in scaling the results to realistic audience sizes. This paper presents a new approach to appreciation measurement based on the analysis of BBC iPlayer on-demand viewing pattern data, such as the timeline of the user’s interactions with the play button, combined with appreciation scores from traditional feedback surveys. This methodology infers implicit viewer appreciation automatically, without adding significant cost or time overheads and without requiring additional input from the participant or the use of intrusive methods, such as facial recognition. The results obtained, based on data from a sample of over 27,000 iPlayer users, show accuracy scores above 90% for predictions generated using computationally efficient models, including Decision Trees and Random Forests. The analysis suggests that the user’s appreciation of a programme can be predicted based on their online viewing behaviour, potentially improving our understanding of the audience.
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