Hyper-community detection in the blogosphere
Most 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 o...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
ACM
2010
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| Subjects: | |
| Online Access: | http://doi.acm.org/10.1145/1878151.1878159 http://hdl.handle.net/20.500.11937/22301 |
| Summary: | Most 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 topicslearnt 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 foraddressing 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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