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Temporal dynamics of communities in social bookmarking systems

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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.

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Authors and Affiliations

  1. Department of Engineering, Salalah College of Technology, P.O. Box 608, Salalah, PC 211, Oman

    Said Kashoob

  2. Department of Computer Science and Engineering, Texas A&M University, College Station, TX, 77843, USA

    James Caverlee

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  1. Said Kashoob
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  2. James Caverlee
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Correspondence to Said Kashoob.

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Kashoob, S., Caverlee, J. Temporal dynamics of communities in social bookmarking systems. Soc. Netw. Anal. Min. 2, 387–404 (2012). https://doi.org/10.1007/s13278-012-0054-z

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