Paper Info
Title
Content analysis meets viewers: linking concept detection with demographics on YouTube.
Abstract
Social image and video sharing provides the opportunity for a user-centric, behavioral auto-understanding of image and video content. We add demographic aspects to this puzzle, i.e. the popularity of content across different ages and genders: employing user comments, we calculate demographic viewership profiles for YouTube clips and provide evidence that these profiles are strongly correlated with semantic concepts appearing in a video. Based on this fact, we outline two approaches that combine video content analysis with demographic aspects: first, we show that concept detection can be used to establish a mapping from content via concepts to viewer demographics (which we refer to as content-based demographics prediction). Second, in case sufficient view statistics already give an estimate of a clip’s audience, they can be used as a demographic signal to disambiguate concept detection in cases of visually similar concepts. We validate the above statements on a dataset of 14,000 YouTube clips covering 105 concepts and commented by 1 mio. users: content-based demographics prediction is shown to provide an accuracy comparable to other information sources (such as a video’s tags or uploader data). Also, demographic signals can improve the accuracy of concept detection significantly (by 47 % compared to a content-only approach).
Year
DOI
Venue
2013
10.1007/s13735-012-0029-x
IJMIR
Keywords
Field
DocType
Concept detection, Video analysis, Demographics prediction, Online video, Content-based video retrieval
Computer science,Popularity,Artificial intelligence,Social image,Video quality,Audience measurement,Content analysis,Information retrieval,Video content analysis,Video sharing,Demographics,Multimedia,Machine learning
Journal
Volume
Issue
ISSN
2
2
2192-662X
Citations 
PageRank 
References 
2
0.38
31
Authors
3
Name
Order
Citations
PageRank
Adrian Ulges132826.61
Damian Borth276449.45
Markus Koch320.38