Knowing Funny: Genre Perception And Categorization In Social Video Sharing

Categorization of online videos is often treated as a tag suggestion task; tags can be generated by individuals or by machine classification. In this paper, we suggest categorization can be determined socially, based on people's interactions around media content without recourse to metadata that are intrinsic to the media object itself. This work bridges the gap between the human perception of genre and automatic categorization of genre in classifying online videos. We present findings from two internet surveys and from follow-up interviews where we address how people determine genre classification for videos and how social framing of video content can alter the perception and categorization of that content. From these findings, we train a Naive Bayes classifier to predict genre categories. The trained classifier achieved 82% accuracy using only social action data, without the use of content or media-specific metadata. We conclude with implications on how we categorize and organize media online as well as what our findings mean for designing and building future tools and interaction experiences.