Hyper-community detection in the blogosphere
Contribuinte(s) |
[Unknown] |
---|---|
Data(s) |
01/01/2010
|
Resumo |
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 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. |
Identificador | |
Idioma(s) |
eng |
Publicador |
ACM |
Relação |
http://dro.deakin.edu.au/eserv/DU:30044549/nguyen-hypercommunity-2010.pdf http://hdl.handle.net/10.1145/1878151.1878159 |
Direitos |
2010, ACM |
Palavras-Chave | #content-based #hyper-community #sentiment-based #social media |
Tipo |
Conference Paper |