Abstract
Unprecedented growth in social bookmarking systems is making accessible the perspectives of millions of users on online content. This makes possible the ability to detect temporal group formation and their transient interests in online social systems. Here, we introduce a community evolution framework for studying and analyzing social bookmarking communities over time. We apply this framework to a large set of social bookmarking data, over 13 million unique postings, collected over a period of 15 weeks. We inspect the temporal dimension of social bookmarking and explore the dynamics of community formation, evolution, and dissolution. We show how our approach captures evolution, dynamics, and relationships among the discovered communities, which has important implications for designing future bookmarking systems, and anticipating user’s future information needs.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
