Analyzing Behavior of the Influentials Across Social Media

The popularity of social media as an information source, in the recent years has spawned several interesting applications, and consequently challenges to using it effectively. Identifying and targeting influential individuals on sites is a crucial way to maximize the returns of advertising and marketing efforts. Recently, this problem has been well studied in the context of blogs, microblogs, and other forms of social media sites. Understanding how these users behave on a social media site and even across social media sites will lead to more effective strategies. In this book chapter, we present existing techniques to identify influential individuals in a social media site. We present a user identification strategy, which can help us to identify influential individuals across sites. Using a combination of these approaches we present a study of the characteristics and behavior of influential individuals across sites. We evaluate our approaches on several of the popular social media sites. Among other interesting findings, we discover that influential individuals on one site are more likely to be influential on other sites as well. We also find that influential users are more likely to connect to other influential individuals.

[1]  David R. Oran,et al.  Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication , 1995, SIGCOMM 1995.

[2]  Reza Zafarani,et al.  Connecting Corresponding Identities across Communities , 2009, ICWSM.

[3]  Elad Yom-Tov,et al.  Serial Sharers: Detecting Split Identities of Web Authors , 2007, PAN.

[4]  R. Merton Social Theory and Social Structure , 1958 .

[5]  Kathy E. Gill How can we measure the influence of the blogosphere? , 2004 .

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

[7]  E. Katz The Two-Step Flow of Communication: An Up-To-Date Report on an Hypothesis , 1957 .

[8]  Ming Li,et al.  Clustering by compression , 2003, IEEE International Symposium on Information Theory, 2003. Proceedings..

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

[10]  Michalis Faloutsos,et al.  On power-law relationships of the Internet topology , 1999, SIGCOMM '99.

[11]  R. Armstrong The Long Tail: Why the Future of Business Is Selling Less of More , 2008 .

[12]  Jacob Goldenberg,et al.  Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth , 2001 .

[13]  E. Rogers,et al.  Communication of innovations: A cross-cultural approach, 2nd ed. , 1971 .

[14]  E. Rogers,et al.  Communication of Innovations; A Cross-Cultural Approach. , 1974 .

[15]  David Liben-Nowell,et al.  The link-prediction problem for social networks , 2007 .

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

[17]  Andrzej Skowron,et al.  Proceedings of the 2005 IEEE / WIC / ACM International Conference on Web Intelligence , 2005 .

[18]  J. Berry The Influentials: One American in Ten Tells the Other Nine How to Vote, Where to Eat, and What to Buy , 2003 .

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

[20]  Yun Chi,et al.  Detecting splogs via temporal dynamics using self-similarity analysis , 2008, TWEB.

[21]  J. Golbeck,et al.  FilmTrust: movie recommendations using trust in web-based social networks , 2006, CCNC 2006. 2006 3rd IEEE Consumer Communications and Networking Conference, 2006..

[22]  Timothy W. Finin,et al.  SVMs for the Blogosphere: Blog Identification and Splog Detection , 2006, AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs.

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

[24]  Krishna P. Gummadi,et al.  Measurement and analysis of online social networks , 2007, IMC '07.

[25]  M. Hitt The Long Tail: Why the Future of Business Is Selling Less of More , 2007 .