Interactive Web Documentaries: A Case Study of Video Viewing Behaviour on iOtok

• Published in: International journal of human-computer interaction 2022-06, Vol.38 (10), p.949-972
• Main Authors: Ducasse, Julie, Kljun, Matjaž, Attygalle, Nuwan T., Pucihar, Klen Čopič
• Format: Article
• Language: English
• Subjects: Algorithms; Case studies; Cluster analysis; Clustering; Documentary films; Streaming media; Vector quantization; Video; Viewing; Webs
• ISSN: 1044-7318; 1532-7590; 1044-7318
• DOI: 10.1080/10447318.2021.1976511

This article explores video-viewing behavior when videos are wrapped in interactive content in the case of iOtok, a 13-episodes web documentary series. The interaction and viewing data were collected over a period of one year, providing a dataset of more than 12,200 total video views by 6000 users. Standard metrics (video views, percentage viewed, number of sessions) show higher active participation for registered users compared to unregistered users. Results also indicate that serialization over multiple weeks is an effective strategy for audience building over a long period of time without negatively affecting video views. In viewing behavior analysis, we focused on three perspectives: (i) regularity (watching on a weekly basis or not), (ii) intensity (number of videos per session), and (iii) order of watching. We performed a perspective based and combined perspectives analysis involving manual coding techniques, rule-based, and k-means clustering algorithms to reveal different user profiles (intermittent, exemplary, detached, enthusiastic users, and nibblers) and highlight further viewing behavior differences (e.g., post-series users binge-watched more than concurrent users during first 13 weeks while the series was weekly released). We discuss how these results can be used to inform the design and promotion of future web documentaries.