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Multimedia semantics: Interactions between content and community

Sundaram, H and Xie, L and De Choudhury, M and Lin, YR and Natsev, A (2012) Multimedia semantics: Interactions between content and community. Proceedings of the IEEE, 100 (9). 2737 - 2758. ISSN 0018-9219

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This paper reviews the state of the art and some emerging issues in research areas related to pattern analysis and monitoring of web-based social communities. This research area is important for several reasons. First, the presence of near-ubiquitous low-cost computing and communication technologies has enabled people to access and share information at an unprecedented scale. The scale of the data necessitates new research for making sense of such content. Furthermore, popular websites with sophisticated media sharing and notification features allow users to stay in touch with friends and loved ones; these sites also help to form explicit and implicit social groups. These social groups are an important source of information to organize and to manage multimedia data. In this article, we study how media-rich social networks provide additional insight into familiar multimedia research problems, including tagging and video ranking. In particular, we advance the idea that the contextual and social aspects of media are as important for successful multimedia applications as is the media content. We examine the inter-relationship between content and social context through the prism of three key questions. First, how do we extract the context in which social interactions occur? Second, does social interaction provide value to the media object? Finally, how do social media facilitate the repurposing of shared content and engender cultural memes? We present three case studies to examine these questions in detail. In the first case study, we show how to discover structure latent in the social media data, and use the discovered structure to organize Flickr photo streams. In the second case study, we discuss how to determine the interestingness of conversationsand of participantsaround videos uploaded to YouTube. Finally, we show how the analysis of visual content, in particular tracing of content remixes, can help us understand the relationship among YouTube participants. For each case, we present an overview of recent work and review the state of the art. We also discuss two emerging issues related to the analysis of social networksrobust data sampling and scalable data analysis. © 2012 IEEE.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Sundaram, H
Xie, L
De Choudhury, M
Lin, YRYURULIN@pitt.eduYURULIN0000-0002-8497-3015
Natsev, A
Date: 1 January 2012
Date Type: Publication
Journal or Publication Title: Proceedings of the IEEE
Volume: 100
Number: 9
Page Range: 2737 - 2758
DOI or Unique Handle: 10.1109/jproc.2012.2191529
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
ISSN: 0018-9219
Date Deposited: 16 Aug 2013 20:45
Last Modified: 20 Mar 2020 14:55


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