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).