DISSECT: Data-Intensive Socially Similar Evolving Community Tracker

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Abstract

This chapter examines the problem of tracking community in social networks inferred from online interactions by tracking evolution of known subgroups over time. Finding subgroups within social networks is important for understanding and possibly influencing the formation and evolution of online communities. A variety of approaches have been suggested to address this problem and the corresponding research literature on centrality, clustering, and optimization methods for finding subgroupings is reviewed. This review will include a critical analysis of the limitations of past approaches. The focus of the chapter will then turn to novel methods for tracking online community interaction. First, the method proposed by Chin and Chignell called SCAN will be briefly introduced, where a combination of heuristic methods is used to identify subgroups in a manner that can potentially scale up to very large social networks. Then, we present the DISSECT method where multiple known subgroups within a social network are tracked in terms of similarity-based cohesiveness over time. The DISSECT method relies on cluster analysis of snapshots of network activity at different points in time followed by similarity analysis of subgroup evolution over successive time periods. The DISSECT method can be supplemented with behavioral measures of sense of community where administration of a questionnaire is feasible. Finally, we conclude the chapter with a discussion on possible applications and use of the DISSECT method.

Keywords

Social Network Online Community Betweenness Centrality Online Social Network Closeness Centrality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgements

We would like to thank the TorCamp group for allowing us to use their Google Groups site for data analysis and the participants for completing the behavioral surveys. The authors would also like to thank Jennifer Keelan and Kumanan Wilson for providing us with the content analysis information from the YouTube vaccination videos shown in Table 4.1.

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Authors and Affiliations

  1. 1.Nokia Research CenterBeijingChina

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