Identifying Latent Indicators of Technical Difficulties from Interaction Data

Research output: Contribution to conferencePaperpeer-review

Abstract

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.

Bibliographical metadata

Original languageEnglish
Publication statusPublished - 20 Aug 2018
EventKDD Workshop on Data Science, Journalism & Media - London, United Kingdom
Event duration: 20 Aug 2018 → …

Workshop

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