Abstract
The emergence of the mediated social web – a distributed network of participants creating rich media content and engaging in interactive conversations through Internet-based communication technologies – has contributed to the evolution of powerful social, economic and cultural change. Online social network sites and blogs, such as Facebook, Twitter, Flickr and LiveJournal, thrive due to their fundamental sense of “community”. The growth of online communities offers both opportunities and challenges for researchers and practitioners. Participation in online communities has been observed to influence people’s behavior in diverse ways ranging from financial decision-making to political choices, suggesting the rich potential for diverse applications. However, although studies on the social web have been extensive, discovering communities from online social media remains challenging, due to the interdisciplinary nature of this subject. In this article, we present our recent work on characterization of communities in online social media using computational approaches grounded on the observations from social science.
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Notes
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A more systematical solution will be presented in next section.
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Acknowledgements
This material is based upon work supported in part by NEC Labs America, an IBM Ph.D. Fellowship and a Kauffman Entrepreneur Scholarship. We are pleased to acknowledge Yun Chi, Shenghuo Zhu, Belle Tseng, Jun Tatemura and Koji Hino, from NEC Labs America, for providing the invaluable advices on community discovery and the NEC Blog dataset. We are indebted to Jimeng Sun, Paul Castro and Ravi Konuru, from IBM T.J. Watson Research Center, for providing advices on tensor analysis and the IBM enterprise data.
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Lin, YR., Sundaram, H., Kelliher, A. (2010). Multi-Relational Characterization of Dynamic Social Network Communities. In: Furht, B. (eds) Handbook of Social Network Technologies and Applications. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7142-5_18
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