Finding subgroups within social networks is important for understanding and possibly
influencing the formation and evolution of online communities. This thesis addresses
the problem of finding cohesive subgroups within social networks inferred from online
interactions. The dissertation begins with a review of relevant literature and identifies
existing methods for finding cohesive subgroups. This is followed by the introduction of the SCAN method for identifying subgroups in online interaction. The SCAN (Social Cohesion Analysis of Networks) methodology involves three steps: selecting the possible members (Select), collecting those members into possible subgroups (Collect) and choosing
the cohesive subgroups over time (Choose). Social network analysis, clustering and
partitioning, and similarity measurement are then used to implement each of the steps.
Two further case studies are presented, one involving the TorCamp Google group and the
other involving YouTube vaccination videos, to demonstrate how the methodology works
in practice. Behavioural measures of Sense of Community and the Social Network Questionnaire are correlated with the SCAN method to demonstrate that the SCAN approach
can find meaningful subgroups. Additional empirical findings are reported. Betweenness
centrality appears to be a useful filter for screening potential subgroup members,
and members of cohesive subgroups have stronger community membership and influence
than others. Subgroups identified using weighted average hierarchical clustering are consistent with the subgroups identified using the more computationally expensive k-plex analysis. The value of similarity measurement in assessing subgroup cohesion over time is demonstrated, and possible problems with the use of Q modularity to identify cohesive subgroups are noted. Applications of this research to marketing, expertise location, and information search are also discussed.
| Identifer | oai:union.ndltd.org:TORONTO/oai:tspace.library.utoronto.ca:1807/17742 |
| Date | 23 September 2009 |
| Creators | Chin, Alvin Yung Chian |
| Contributors | Chignell, Mark |
| Source Sets | University of Toronto |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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