Measuring the influence of social networks on information diffusion on blogspheres

As a typical social media in web2.0 era, blogs have become more and more important to information diffusion. Different from the traditional news, the information spread on blogs is primarily driven by users and their relations. According to this phenomenon, this paper addresses the novel problem of measuring the influence of social structures on information diffusion. This paper extracts the hidden communities and information diffusion networks on blogs, and proposes a novel quantitative measurement to investigate the influence of social networks structure on diffusion networks. The proposed methods measure the influence by utilizing a simple but effective graph similarity measuring method. The experiments demonstrate the effectiveness of the proposed algorithms and measurements, and discover the correlation between social networks and the diffusion of the “interest” topics, which indicates the necessity of using social structures for information detection and tracking on blogs.

[1]  Juan Julián Merelo Guervós,et al.  Mapping weblog communities , 2003, ArXiv.

[2]  Iraklis Varlamis,et al.  BlogRank: ranking weblogs based on connectivity and similarity features , 2006, AAA-IDEA '06.

[3]  John Scott.  Social Network Analysis , 1988 .

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

[5]  Paul Van Dooren,et al.  A MEASURE OF SIMILARITY BETWEEN GRAPH VERTICES . WITH APPLICATIONS TO SYNONYM EXTRACTION AND WEB SEARCHING , 2002 .

[6]  Pattie Maes,et al.  Social information filtering: algorithms for automating “word of mouth” , 1995, CHI '95.

[7]  U. Brandes.  A faster algorithm for betweenness centrality , 2001 .

[8]  Kazunari Ishida.  Extracting Latent Weblog Communities-A Partitioning Algorithm for Bipartite Graphs - , 2005 .

[9]  Erhard Rahm,et al.  Similarity flooding: a versatile graph matching algorithm and its application to schema matching , 2002, Proceedings 18th International Conference on Data Engineering.

[10]  Andreas Krause,et al.  Cost-effective outbreak detection in networks , 2007, KDD '07.

[11]  Ramanathan V. Guha,et al.  Information diffusion through blogspace , 2004, WWW '04.

[12]  Charles L. Wayne.  Topic Detection & Tracking ( TDT ) Overview & Perspective , 1998 .

[13]  Kristina Lerman,et al.  Social Networks and Social Information Filtering on Digg , 2006, ICWSM.

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

[15]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Wang Ting.  Research on the Approximation Algorithms for the Betweenness Property Computation on Complex Social Networks , 2008 .

[17]  Thorsten Brants,et al.  Multiple Similarity Measures and Source-Pair Information in Story Link Detection , 2004, HLT-NAACL.

[18]  Mor Naaman,et al.  Towards extracting flickr tag semantics , 2007, WWW '07.

[19]  Lada A. Adamic,et al.  Tracking information epidemics in blogspace , 2005, The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05).