Hyper-community detection in the blogosphere

Thin Nguyen, Dinh Phung, Brett Adams, Truyen Tran, Svetha Venkatesh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

15 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationWSM'10 - Proceedings of the 2nd ACM SIGMM Workshop on Social Media, Co-located with ACM Multimedia 2010
Pages21-26
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2010
Externally publishedYes
Event2nd ACM SIGMM Workshop on Social Media, WSM'10, Co-located with ACM Multimedia 2010 - Firenze, Italy
Duration: 25 Oct 201025 Oct 2010

Publication series

NameWSM'10 - Proceedings of the 2nd ACM SIGMM Workshop on Social Media, Co-located with ACM Multimedia 2010

Conference

Conference2nd ACM SIGMM Workshop on Social Media, WSM'10, Co-located with ACM Multimedia 2010
CountryItaly
CityFirenze
Period25/10/1025/10/10

Keywords

  • Content-based
  • Hyper-community
  • Sentiment-based
  • Social media

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