Identifying on-line groups based on content and collective behavioral patterns

Dave Engel, Michelle Gregory, Eric Bell, Liam Mcgrath

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

1 Scopus citations

Abstract

Online communities, or groups, have largely been defined based on links, page rank, and eigenvalues. In this paper we explore identifying abstract groups, groups where member's interests and online footprints are similar but they are not necessarily connected to one another explicitly. We use a combination of structural information and content information from posts and their comments to build a footprint for groups. We find that these variables do a good job at identifying groups, placing members within a group, and help determine the appropriate granularity for group boundaries.

Original languageEnglish
Title of host publicationProceedings of the IADIS International Conferences - Web Based Communities and Social Media 2011, Social Media 2011, Internet Applications and Research 2011, Part of the IADIS, MCCSIS 2011
Pages101-108
Number of pages8
StatePublished - 2011
Externally publishedYes
EventIADIS International Conferences - Web Based Communities and Social Media 2011, Social Media 2011, Internet Applications and Research 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011 - Rome, Italy
Duration: Jul 22 2011Jul 24 2011

Publication series

NameProceedings of the IADIS International Conferences - Web Based Communities and Social Media 2011, Social Media 2011, Internet Applications and Research 2011, Part of the IADIS, MCCSIS 2011

Conference

ConferenceIADIS International Conferences - Web Based Communities and Social Media 2011, Social Media 2011, Internet Applications and Research 2011, Part of the IADIS Multi Conference on Computer Science and Information Systems 2011, MCCSIS 2011
Country/TerritoryItaly
CityRome
Period07/22/1107/24/11

Keywords

  • Abstract groups
  • Anomaly detection
  • Clustering
  • Content-based
  • Footprint

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