A social network analysis and mining methodology for the monitoring of specific domains in the blogosphere

Whenever the question arises of how a product, a personality, a technology or some other specific entity is perceived by the public, the blogosphere is a very good source of information. This is what usually interests business users from marketing or PR. Modern search services offer a rich set of tools to monitor or track the blogosphere as a whole, but the analysis with respect to a certain domain is very limited. In this paper, we lay some foundations to aggregate blog articles of a specific domain from multiple search services, to analyze the social authorities of articles and blogs, and to monitor the attention articles of the domain receive over time. These are the building blocks required for a monitoring application that presents users the topics and trends in a specific domain along with the currently most interesting articles. This methodology has been instantiated and combined with additional textual analysis methods to create highly automated business intelligence application in the context of the Social Media Miner project.

[1]  Ravi Kumar,et al.  On the Bursty Evolution of Blogspace , 2003, WWW '03.

[2]  Andreas Dengel,et al.  Automatic Sentiment Monitoring of Specific Topics in the Blogosphere , 2010, NyNaK.

[3]  Eytan Adar,et al.  GUESS: a language and interface for graph exploration , 2006, CHI.

[4]  Eytan Adar,et al.  Implicit Structure and the Dynamics of Blogspace , 2004 .

[5]  B Skyrms,et al.  A dynamic model of social network formation. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Peter Wortmann.  Topic-Based Blog Article Search for Trend Detection , 2009 .

[7]  Stephan Baumann,et al.  A Journey to the Core of the Blogosphere , 2010, From Sociology to Computing in Social Networks.

[8]  Dragomir R. Radev,et al.  Content Based Recommendation and Summarization in the Blogosphere , 2009, ICWSM.

[9]  Christos Faloutsos,et al.  Modeling Blog Dynamics , 2009, ICWSM.

[10]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[11]  Rafael Schirru,et al.  Domain-Specific Identification of Topics and Trends in the Blogosphere , 2010, ICDM.

[12]  Tanya Y. Berger-Wolf,et al.  A framework for analysis of dynamic social networks , 2006, KDD '06.

[13]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[14]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[15]  Philip S. Yu,et al.  Identifying the influential bloggers in a community , 2008, WSDM '08.

[16]  Cameron A. Marlow.  Audience, structure and authority in the weblog community , 2004 .

[17]  Inna Kouper,et al.  Conversations in the Blogosphere: An Analysis "From the Bottom Up" , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[18]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[19]  Jennifer Jie Xu,et al.  Mining communities and their relationships in blogs: A study of online hate groups , 2007, Int. J. Hum. Comput. Stud..

[20]  Matthieu Latapy,et al.  Basic notions for the analysis of large two-mode networks , 2008, Soc. Networks.

[21]  Andreas Dengel,et al.  Mining shared social media links to support clustering of blog articles , 2011, 2011 International Conference on Computational Aspects of Social Networks (CASoN).

[22]  Stephan Baumann,et al.  Identifying and Analysing Germany's Top Blogs , 2008, KI.