Identifying Latent Indicators of Technical Difficulties from Interaction Data

Jonathan Carlton, Joshua Woodcock, Andy Brown, Caroline Jay, John Keane

Research output: Contribution to conferencePaperpeer-review


A significant amount of resource is spent in maximising the retention levels of consumers of online media content. Moreover, it is considered a success if the consumer stays engaged throughout and consumes the media in its entirety. Despite this, unforeseen circumstances may hinder the ability of the consumer to enjoy the produced content and lead to low overall retention rates. In this paper, we explore interaction data, collected from an interactive media experience, to discover user behaviours that serve as indicators for technical difficulties. Detecting technical faults, such as video buffering, from the analysis of interaction data could o er the ability to provide corrective suggestions. It also, crucially, helps us to determine when dropout is caused by factors other than the content itself. We report that users who experienced and reported video- related faults share similar descriptive statistics to those who did not report faults; however, analysis of discrete sequences of events demonstrates that there are, in actuality, fundamental differences between the two groups.
Original languageEnglish
Publication statusPublished - 20 Aug 2018
EventKDD Workshop on Data Science, Journalism & Media - London, United Kingdom
Duration: 20 Aug 2018 → …


WorkshopKDD Workshop on Data Science, Journalism & Media
Abbreviated titleDJSM
Country/TerritoryUnited Kingdom
Period20/08/18 → …


  • Interaction Data Mining
  • Click-based Data
  • Technical Difficulties
  • Human-computer interaction


Dive into the research topics of 'Identifying Latent Indicators of Technical Difficulties from Interaction Data'. Together they form a unique fingerprint.

Cite this