ABSTRACT
This chapter describes the collective intelligence behavior of web users who share and watch video content. It discusses the aggregated users’ video activity exhibiting characteristic patterns that may be used in order to infer importantvideo scenes, leading to collective intelligence concerning the video content. The chapter presents a method that detects collective behavior of users’ activity via the detection of characteristic patterns in the corresponding signal monitoring users’ activity. The methodology presented may treat general users’ interactions for a specific content, by interpreting these interactions as an explicit time series. Many individuals, organizations, and academic institutions are making lectures, documentaries, and how-to videos available online. Content-based information retrieval uses automated techniques to analyze actual video content. In comparison to the more legacy content-based techniques, there are fewer works on user-based analysis of information retrieval for video content. Collective intelligence is attributed to the claim of being able to understand the importance of video content from users’ interactions with the player.
