Inferring User Engagement from Interaction Data

Jonathan Carlton, Andy Brown, Caroline Jay, John Keane

Research output: Contribution to conferencePaperpeer-review


This paper presents preliminary results of a study designed to quantify users' engagement levels with interactive media content, through self-reported measures and interaction data. The broad hypothesis of the study is that interaction data can be used to predict the level of engagement felt by the user. The challenge addressed in this work is to explore the effectiveness of interaction data to act as a proxy for engagement levels and reveal what that data shows about engagement with media content. Preliminary results suggest several interesting insights about participants engagement and behaviour. Crucially, temporal statistics support the hypothesis that the participant making use of the controls in the interactive, video-based experience positively correlates with higher engagement.
Original languageEnglish
Publication statusPublished - May 2019
EventACM 2019 CHI Conference on Human Factors in Computing Systems: Extended Abstracts - Glasgow, United Kingdom
Duration: 4 May 20199 May 2019


ConferenceACM 2019 CHI Conference on Human Factors in Computing Systems
Abbreviated titleCHI
Country/TerritoryUnited Kingdom
Internet address


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