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Hyper-community detection in the blogosphere
conference contribution
posted on 2010-01-01, 00:00 authored by Thin NguyenThin Nguyen, Quoc-Dinh Phung, B Adams, Truyen TranTruyen Tran, Svetha VenkateshSvetha VenkateshMost existing work on learning community structure in social network is graph-based whose links among the members are often represented as an adjacency matrix, encoding direct pairwise associations between members. In this paper, we propose a method to group online communities in blogosphere based on the topics learnt from the content blogged. We then consider a different type of online community formulation - the sentiment-based grouping of online communities. The problem of sentiment-based clustering for community structure discovery is rich with many interesting open aspects to be explored. We propose a novel approach for addressing hyper-community detection based on users' sentiment. We employ a nonparametric clustering to automatically discover hidden hyper-communities and present the results obtained from a large dataset.
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Event
ACM SIGMM Workshop on Social Media (2nd : 2010 : Firenze, Italy)Pagination
21 - 26Publisher
ACMLocation
Firenze, ItalyPlace of publication
New York, N. Y.Start date
2010-10-25ISBN-13
9781450301732Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2010, ACMTitle of proceedings
WSM 2010 : Proceedings of the 2nd ACM SIGMM Workshop on Social MediaUsage metrics
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