User Role Analysis in Online Social Networks Based on Dirichlet Process Mixture Models

The large number of SNS users brings marketers and managers huge opportunities and tough challenges simultaneously to extract managerial implications from SNS user behaviors. To gain insight into user behaviors, researchers divide users into roles (i.e. user groups) to analyze the difference of user behaviors between distinct roles. In traditional role discovery algorithms, the number of roles is intractable and predefined. User features implied in unstructured text data have been rarely used. In this paper, we propose a Dirichlet Process Mixture Model to automatically optimize the number of roles and integrate features mined from text data to analyze user roles.

[1]  Anthony J. T. Lee,et al.  Discovering content-based behavioral roles in social networks , 2014, Decis. Support Syst..

[2]  Marc A. Smith,et al.  A Conceptual and Operational Definition of 'Social Role' in Online Community , 2009 .

[3]  Matthew K. O. Lee,et al.  A theoretical model of intentional social action in online social networks , 2010, Decis. Support Syst..

[4]  Marc A. Smith,et al.  A Conceptual and Operational Definition of 'Social Role' in Online Community , 2009, 2009 42nd Hawaii International Conference on System Sciences.

[5]  Ohbyung Kwon,et al.  An empirical study of the factors affecting social network service use , 2010, Comput. Hum. Behav..

[6]  Scott A. Golder,et al.  SOCIAL ROLES IN ELECTRONIC COMMUNITIES , 2004 .

[7]  Weiguo Fan,et al.  Determinants of users' continuance of social networking sites: A self-regulation perspective , 2014, Inf. Manag..

[8]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994 .

[9]  Michael Salter-Townshend,et al.  Role Analysis in Networks Using Mixtures of Exponential Random Graph Models , 2015, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.

[10]  B. Schölkopf,et al.  Hierarchical Dirichlet Processes with Random Effects , 2007 .

[11]  Jie Tang,et al.  Probabilistic Community and Role Model for Social Networks , 2015, KDD.

[12]  Craig Ross,et al.  Personality and motivations associated with Facebook use , 2009, Comput. Hum. Behav..

[13]  Zach W. Y. Lee,et al.  Self-disclosure in social networking sites: The role of perceived cost, perceived benefits and social influence , 2015, Internet Res..

[14]  Danyel Fisher,et al.  Visualizing the Signatures of Social Roles in Online Discussion Groups , 2007, J. Soc. Struct..

[15]  Elizabeth M. Daly,et al.  Decomposing Discussion Forums using Common User Roles , 2010 .

[16]  Ryan A. Rossi,et al.  Role Discovery in Networks , 2014, IEEE Transactions on Knowledge and Data Engineering.

[17]  Michael I. Jordan,et al.  Hierarchical Dirichlet Processes , 2006 .

[18]  Marcelo Maia,et al.  Identifying user behavior in online social networks , 2008, SocialNets '08.