Coolhunting for trends on the Web

This paper introduces a new way of measuring the popularity of brand names and famous people such as movie stars, politicians, and business executives. It is based upon the premise that in todaypsilas Internet economy the Web displays a mirror of the real world. Our system uses TeCFlow, a social networking tool developed for the last four years at MIT, to measure popularity and influence of brands and stars by looking at their relative position on the Web. It is based on the simple insight: ldquoYou are who links to yourdquo. It applies the Social Network Analysis( SNA) metric of ldquobetweennessc entralityrdquo to the Web, looking at the linking structure of Web sites to find how Web pages discussing brands and stars are connected. It uses high-betweenness Web sites returned to a search engine query for a brand or star name as a proxy for the significance of this brand or star.

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

[2]  Amitai Etzioni ~, Routledge , 2004 .

[3]  Lada A. Adamic,et al.  Friends and neighbors on the Web , 2003, Soc. Networks.

[4]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[5]  Peter A. Gloor,et al.  Capturing team dynamics through temporal social surfaces , 2005, Ninth International Conference on Information Visualisation (IV'05).

[6]  David M. Pennock,et al.  The structure of broad topics on the web , 2002, WWW.

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

[8]  M. Dodge,et al.  Mapping Cyberspace , 2000 .

[9]  Peter A. Gloor,et al.  Swarm Creativity: Competitive Advantage Through Collaborative Innovation Networks , 2006 .

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

[11]  Yan Zhao,et al.  Analyzing Actors and Their Discussion Topics by Semantic Social Network Analysis , 2006, Tenth International Conference on Information Visualisation (IV'06).

[12]  Sougata Mukherjea,et al.  Organizing topic-specific web information , 2000, HYPERTEXT '00.

[13]  Jon M. Kleinberg,et al.  Mining the Web's Link Structure , 1999, Computer.

[14]  Albert-László Barabási,et al.  Linked - how everything is connected to everything else and what it means for business, science, and everyday life , 2003 .

[15]  Mohammad Al Hasan,et al.  Link prediction using supervised learning , 2006 .

[16]  Bing Liu,et al.  Visualizing web site comparisons , 2002, WWW '02.

[17]  Peter A. Gloor,et al.  Coolhunting: Chasing Down the Next Big Thing , 2007 .

[18]  Marc A. Smith,et al.  Visualization components for persistent conversations , 2001, CHI.

[19]  David M. Pennock,et al.  Winners don't take all: Characterizing the competition for links on the web , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[20]  John Scott What is social network analysis , 2010 .

[21]  Peter A. Gloor,et al.  TeCFlow – A Temporal Communication Flow Visualizer for Social Network Analysis , 2004 .

[22]  R. Kitchin,et al.  The Atlas of Cyberspace , 2001 .