Community-Oriented Models and Applications for the Social Web



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The past few years have seen the rapid rise of all things "social" on the web from the growth of online social networks like Facebook, to user-contributed content sites like Flickr and YouTube, to social bookmarking services like Delicious, among many others. Whereas traditional approaches to organizing and accessing the web?s massive amount of information have focused on content-based and link-based approaches, these social systems offer rich opportunities for user-based and community-based exploration and analysis of the web by building on the unprecedented access to the interests and perspectives of millions of users.

We focus here on the challenge of modeling and mining social bookmarking systems, in which resources are enriched by large-scale socially generated metadata (?tags?) and contextualized by the user communities that are associated with the resources. Our hypothesis is that an underlying social collective intelligence is embedded in the uncoordinated actions of users on social bookmarking services, and that this social collective intelligence can be leveraged for enhanced web-based information discovery and knowledge sharing. Concretely, we posit the existence of underlying implicit communities in these social bookmarking systems that drive the social bookmarking process which can provide a foundation for community-based organization of web resources.

To that end, we make three contributions: ? First, we propose a pair of novel probabilistic generative models for describing and modeling community-oriented social bookmarking. We show how these models enable effective extraction of meaningful communities over large real world social bookmarking services. ? Second, we develop two frameworks for community-based web information browsing and search that are based on these community-oriented social bookmarking models. We show how both achieve improved discovery and exploration of the social web. ? Third, we introduce a community evolution framework for studying and analyzing social bookmarking communities over time. We explore the temporal dimension of social bookmarking and explore the dynamics of community formation, evolution, and dissolution. By uncovering implicit communities, putting them to use in an application scenario (search and browsing), and analyzing them over time, this dissertation provides a foundation for the study of how social knowledge networks are self-organized, a deeper understanding and appreciation of the factors impacting collective intelligence, and the creation of new information access algorithms for leveraging these communities.