The Impact of Measurement Time on Subgroup Detection in Online Communities

  • Sam Zeini
  • Tilman Göhnert
  • Tobias Hecking
  • Lothar Krempel
  • H. Ulrich Hoppe
Chapter
Part of the Lecture Notes in Social Networks book series (LNSN)

Abstract

More and more communities use internet based services and infrastructure for communication and collaboration. All these activities leave digital traces that are of interest for research as real world data sources that can be processed automatically or semi-automatically. Since productive online communities (such as open source developer teams) tend to support the establishment of ties between actors who work on or communicate about the same or similar objects, social network analysis is a frequently used research methodology in this field. A typical application of Social Network Analysis (SNA) techniques is the detection of cohesive subgroups of actors (also called “community detection”. We were particularly interested in such methods that allow for the detection of overlapping clusters, which is the case with the Clique Percolation Method (CPM) and Link Community detection (LC). We have used these two methods to analyze data from some open source developer communities (mailing lists and log files) and have compared the results for varied time windows of measurement. The influence of the time span of data capturing/aggregation can be compared to photography: A certain minimal window size is needed to get a clear image with enough “light” (i.e. dense enough interaction data), whereas for very long time spans the image will be blurred because subgroup membership will indeed change during the time span (corresponding to a moving target). In this sense, our target parameter is “resolution” of subgroup structures. We have identified several indicators for good resolution. In general, this value will vary for different types of communities with different communication frequency and behavior. Following our findings, an explicit analysis and comparison of the influence of time window for different communities may be used to better adjust analysis techniques for the communities at hand.

Notes

Acknowledgments

The authors want to thank to Dan Suthers for the useful comments on the draft version of the chapter. We thank Umar Tarar and Ziyyad Qasim for their contributions to the implementation of the CPM-based analysis. We also thank Evelyn Fricke for the first implementation of the Link Communities analysis used in our study.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sam Zeini
    • 1
  • Tilman Göhnert
    • 1
  • Tobias Hecking
    • 1
  • Lothar Krempel
    • 2
  • H. Ulrich Hoppe
    • 1
  1. 1.University Duisburg-EssenDuisburg, EssenGermany
  2. 2.Max-Planck-Institute for the Study of SocietiesCologneGermany

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