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Content analysis meets viewers: linking concept detection with demographics on YouTube

Content analysis meets viewers: linking concept detection with demographics on YouTube 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). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Multimedia Information Retrieval Springer Journals

Content analysis meets viewers: linking concept detection with demographics on YouTube

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Publisher
Springer Journals
Copyright
Copyright © 2012 by Springer-Verlag London
Subject
Computer Science; Multimedia Information Systems; Information Storage and Retrieval; Information Systems Applications (incl. Internet); Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Computer Science, general
ISSN
2192-6611
eISSN
2192-662X
DOI
10.1007/s13735-012-0029-x
Publisher site
See Article on Publisher Site

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).

Journal

International Journal of Multimedia Information RetrievalSpringer Journals

Published: Dec 29, 2012

References