Discovering target groups in social networking sites: An effective method for maximizing joint influential power

With the tremendous popularity of social networking sites in this era of Web 2.0, increasingly more users are contributing their comments and opinions about products, people, organizations, and many other entities. These online comments often have direct influence on consumers' buying decisions and the public's impressions of enterprises. As a result, enterprises have begun to explore the feasibility of using social networking sites as platforms to conduct targeted marking and enterprise reputation management for e-commerce and e-business. As indicated from recent marketing research, the joint influential power of a small group of active users could have considerable impact on a large number of consumers' buying decisions and the public's perception of the capabilities of enterprises. This paper illustrates a novel method that can effectively discover the most influential users from social networking sites (SNS). In particular, the general method of mining the influence network from SNS and the computational models of mathematical programming for discovering the user groups with max joint influential power are proposed. The empirical evaluation with real data extracted from social networking sites shows that the proposed method can effectively identify the most influential groups when compared to the benchmark methods. This study opens the door to effectively conducting targeted marketing and enterprise reputation management on social networking sites.

[1]  Yung-Ming Li,et al.  Identifying influential reviewers for word-of-mouth marketing , 2010, Electron. Commer. Res. Appl..

[2]  Michel X. Goemans,et al.  Semideenite Programming in Combinatorial Optimization , 1999 .

[3]  Bin Wang,et al.  From virtual community members to C2C e-commerce buyers: Trust in virtual communities and its effect on consumers' purchase intention , 2010, Electron. Commer. Res. Appl..

[4]  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.

[5]  Masahiro Kimura,et al.  Extracting Influential Nodes for Information Diffusion on a Social Network , 2007, AAAI.

[6]  Matthew Richardson,et al.  Mining the network value of customers , 2001, KDD '01.

[7]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[8]  David P. Williamson,et al.  Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming , 1995, JACM.

[9]  Ingoo Han,et al.  The Effect of On-Line Consumer Reviews on Consumer Purchasing Intention: The Moderating Role of Involvement , 2007, Int. J. Electron. Commer..

[10]  Robert B. Allen,et al.  Analyzing the Propagation of Influence and Concept Evolution in Enterprise Social Networks through Centrality and Latent Semantic Analysis , 2008, PAKDD.

[11]  Vahab S. Mirrokni,et al.  Optimal marketing strategies over social networks , 2008, WWW.

[12]  Wei Chen,et al.  Efficient influence maximization in social networks , 2009, KDD.

[13]  Jure Leskovec,et al.  Empirical comparison of algorithms for network community detection , 2010, WWW '10.

[14]  Dina Mayzlin,et al.  Promotional Chat on the Internet , 2006 .

[15]  A. Vespignani,et al.  The architecture of complex weighted networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Huajun Chen,et al.  Mining Target Marketing Groups From Users' Web of Trust on Epinions , 2008, AAAI Spring Symposium: Social Information Processing.

[17]  Hsinchun Chen,et al.  CrimeNet explorer: a framework for criminal network knowledge discovery , 2005, TOIS.

[18]  Jon M. Kleinberg,et al.  Inferring Web communities from link topology , 1998, HYPERTEXT '98.

[19]  Jennifer Golbeck,et al.  Computing and Applying Trust in Web-based Social Networks , 2005 .

[20]  Harikesh S. Nair,et al.  Empirical Analysis of Indirect Network Effects in the Market for Personal Digital Assistants , 2004 .

[21]  Peter R. Dickson,et al.  On-line Market Research , 2001, Int. J. Electron. Commer..

[22]  M. Newman.  Analysis of weighted networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[23]  Ilan Newman,et al.  An exact almost optimal algorithm for target set selection in social networks , 2009, EC '09.

[24]  C. Lee Giles,et al.  Efficient identification of Web communities , 2000, KDD '00.

[25]  Paolo Avesani,et al.  Trust-aware recommender systems , 2007, RecSys '07.

[26]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[27]  Matthew Richardson,et al.  Mining knowledge-sharing sites for viral marketing , 2002, KDD.

[28]  C. Lee Giles,et al.  Self-Organization and Identification of Web Communities , 2002, Computer.

[29]  Éva Tardos,et al.  Influential Nodes in a Diffusion Model for Social Networks , 2005, ICALP.

[30]  Tian Hong,et al.  A new network core mining method based on node core influence degree , 2010, 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE).

[31]  Michael Trusov,et al.  Determining Influential Users in Internet Social Networks , 2010 .

[32]  M. Sarvary,et al.  Network Effects and Personal Influences: The Diffusion of an Online Social Network , 2011 .

[33]  Eyal Even-Dar,et al.  A note on maximizing the spread of influence in social networks , 2007, Inf. Process. Lett..

[34]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[35]  Tore Opsahl,et al.  Prominence and control: the weighted rich-club effect. , 2008, Physical review letters.

[36]  Harikesh S. Nair,et al.  Asymmetric Social Interactions in Physician Prescription Behavior: The Role of Opinion Leaders , 2008 .

[37]  Frans Feldberg,et al.  Social Network Influences on Technology Acceptance: A Matter of Tie Strength, Centrality and Density , 2010, Bled eConference.

[38]  Stefan M. Wild,et al.  Maximizing influence in a competitive social network: a follower's perspective , 2007, ICEC.

[39]  Santosh S. Vempala,et al.  On clusterings: Good, bad and spectral , 2004, JACM.

[40]  Gianfranco Walsh,et al.  Electronic Word-of-Mouth: Motives for and Consequences of Reading Customer Articulations on the Internet , 2003, Int. J. Electron. Commer..

[41]  Huaping Chen,et al.  Credibility of Electronic Word-of-Mouth: Informational and Normative Determinants of On-line Consumer Recommendations , 2009, Int. J. Electron. Commer..

[42]  Hongyuan Zha,et al.  Probabilistic models for discovering e-communities , 2006, WWW '06.

[43]  Yinyu Ye,et al.  DSDP5: Software for Semidefinite Programming , 2005 .

[44]  Robert E. Tarjan,et al.  Graph Clustering and Minimum Cut Trees , 2004, Internet Math..

[45]  Paul R. Cohen,et al.  Maximizing Influence Propagation in Networks with Community Structure , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[46]  Joseph P. Forgas,et al.  Social Influence: Direct and Indirect Processes , 2001 .

[47]  Jerker Denrell,et al.  Indirect Social Influence , 2008, Science.

[48]  Chrysanthos Dellarocas,et al.  The Digitization of Word-of-Mouth: Promise and Challenges of Online Feedback Mechanisms , 2003, Manag. Sci..

[49]  Claudio Castellano,et al.  Defining and identifying communities in networks. , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[50]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[51]  Eric K. Clemons,et al.  Do Online Reviews Reflect a Product's True Perceived Quality? - An Investigation of Online Movie Reviews Across Cultures , 2010, 2010 43rd Hawaii International Conference on System Sciences.