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 |
| _version_ | 1848750832294559744 |
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| author | Nguyen, Thin Phung, Dinh Adams, Brett Tran, Truyen Venkatesh, Svetha |
| author2 | S. Boll |
| author_facet | S. Boll Nguyen, Thin Phung, Dinh Adams, Brett Tran, Truyen Venkatesh, Svetha |
| author_sort | Nguyen, Thin |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T07:43:06Z |
| format | Conference Paper |
| id | curtin-20.500.11937-22301 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:43:06Z |
| publishDate | 2010 |
| publisher | ACM |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-223012023-01-13T07:56:30Z Hyper-community detection in the blogosphere Nguyen, Thin Phung, Dinh Adams, Brett Tran, Truyen Venkatesh, Svetha S. Boll S. Hoi J. Luo R. van Zwol sentiment-based Information Search and Retrieval social media hyper-community content-based 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. 2010 Conference Paper http://hdl.handle.net/20.500.11937/22301 http://doi.acm.org/10.1145/1878151.1878159 ACM restricted |
| spellingShingle | sentiment-based Information Search and Retrieval social media hyper-community content-based Nguyen, Thin Phung, Dinh Adams, Brett Tran, Truyen Venkatesh, Svetha Hyper-community detection in the blogosphere |
| title | Hyper-community detection in the blogosphere |
| title_full | Hyper-community detection in the blogosphere |
| title_fullStr | Hyper-community detection in the blogosphere |
| title_full_unstemmed | Hyper-community detection in the blogosphere |
| title_short | Hyper-community detection in the blogosphere |
| title_sort | hyper-community detection in the blogosphere |
| topic | sentiment-based Information Search and Retrieval social media hyper-community content-based |
| url | http://doi.acm.org/10.1145/1878151.1878159 http://hdl.handle.net/20.500.11937/22301 |