tagging, communities, vocabulary, evolution
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
Watching together: integrating text chat with video
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Crowdsourcing user studies with Mechanical Turk
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Zync: the design of synchronized video sharing
Proceedings of the 2007 conference on Designing for User eXperiences
MM '08 Proceedings of the 16th ACM international conference on Multimedia
A tag recommendation system for folksonomy
Proceedings of the 2nd ACM workshop on Social web search and mining
Context-oriented web video tag recommendation
Proceedings of the 19th international conference on World wide web
Content-based tag generation to enable a tag-based collaborative tv-recommendation system.
Proceedings of the 8th international interactive conference on Interactive TV&Video
Image classification using the web graph
Proceedings of the international conference on Multimedia
Effective web video clustering using playlist information
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Watching and talking: media content as social nexus
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Proceedings of the 9th International Symposium on Open Collaboration
Understanding in-video dropouts and interaction peaks inonline lecture videos
Proceedings of the first ACM conference on Learning @ scale conference
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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.