PALADIN: A Pattern Based Approach to Knowledge Discovery in Digital Social Networks

Digital media are used to facilitate social structures thus building digital social networks. Disturbances in such networks occur on different levels (egocentric level, subgroup level, network) and have to be analyzed in the multidimensional context of reference disciplines like sociology and knowledge management. This paper presents a first repository of disturbance patterns for the analysis of digital social networks. Based on the Actor-Network Theory and the Social Network Analysis, new socio-theoretical models for handling complex media settings were developed. On these models a pattern language is defined to describe multidimensional disturbance patterns and to store them in a newly developed pattern repository. The core of the pattern language is the formal expression language for pattern (FELP) which used to specify the structural and the content-specific properties of digital social networks. Results can be visualized with open source graph visualization software. To evaluate the approach a case study has been performed in a repository containing 118 mailing lists and 17359 individuals. Patterns like troll, spammer and burst have been applied successfully.

[1]  Albert-László Barabási,et al.  The physics of the Web , 2001 .

[2]  D. Watts,et al.  Small Worlds: The Dynamics of Networks between Order and Randomness , 2001 .

[3]  Jürgen Göres.  PALADIN: A Pattern-based Approach to Large-scale Dynamic Information Integration , 2005, Grundlagen von Datenbanken.

[4]  Vladimir Batagelj,et al.  Centrality in Social Networks , 1993 .

[5]  Danyel Fisher,et al.  Picturing Usenet: Mapping Computer-Mediated Collective Action , 2005, J. Comput. Mediat. Commun..

[6]  Alain Degenne,et al.  Introducing Social Networks , 1999 .

[7]  Max Jacobson,et al.  A Pattern Language: Towns, Buildings, Construction , 1981 .

[8]  Danyel Fisher,et al.  You Are Who You Talk To: Detecting Roles in Usenet Newsgroups , 2006, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).

[9]  T. R. Madanmohan,et al.  Roles and Knowledge Management in Online Technology Communities: An Ethnography Study , 2002, Int. J. Web Based Communities.

[10]  Martin H. Levinson.  Linked: The New Science of Networks , 2004 .

[11]  R. Parikh.  Towards a Theory of Social Software , 2002 .

[12]  R. Hanneman.  Introduction to Social Network Methods , 2001 .

[13]  Paul Dourish,et al.  From Technical Dependencies to Social Dependencies , 2004 .

[14]  Mark S. Granovetter.  The Strength of Weak Ties , 1973, American Journal of Sociology.

[15]  Andrea Scharnhorst,et al.  Complex Networks and the Web: Insights From Nonlinear Physics , 2006, J. Comput. Mediat. Commun..

[16]  Peter Mika,et al.  Flink: Semantic Web technology for the extraction and analysis of social networks , 2005, J. Web Semant..

[17]  Steven B. Andrews,et al.  Structural Holes: The Social Structure of Competition , 1995, The SAGE Encyclopedia of Research Design.

[18]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .