Multimedia Semantics: Interactions Between Content and Community
Venue
Proceedings of the IEEE, vol. 100, no. 9 (2012)
Publication Year
2012
Authors
Hari Sundaram, Lexing Xie, Munmun De Choudhury, Yu-Ru Lin, Apostol Natsev
BibTeX
Abstract
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
interrelationship 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
conversations---and of participants---around 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
networks---robust data sampling and scalable data analysis.
