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Determining Influential Users in Internet Social Networks

Determining Influential Users in Internet Social Networks The success of Internet social networking sites depends on the number and activity levels of their user members. Although users typically have numerous connections to other site members (i.e., “friends”), only a fraction of those so-called friends may actually influence a member's site usage. Because the influence of potentially hundreds of friends needs to be evaluated for each user, inferring precisely who is influential—and, therefore, of managerial interest for advertising targeting and retention efforts—is difficult. The authors develop an approach to determine which users have significant effects on the activities of others using the longitudinal records of members' log-in activity. They propose a nonstandard form of Bayesian shrinkage implemented in a Poisson regression. Instead of shrinking across panelists, strength is pooled across variables within the model for each user. The approach identifies the specific users who most influence others' activity and does so considerably better than simpler alternatives. For the social networking site data, the authors find that, on average, approximately one-fifth of a user's friends actually influence his or her activity level on the site. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Marketing Research SAGE

Determining Influential Users in Internet Social Networks

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References (30)

Publisher
SAGE
Copyright
© 2010 American Marketing Association
ISSN
0022-2437
eISSN
1547-7193
DOI
10.1509/jmkr.47.4.643
Publisher site
See Article on Publisher Site

Abstract

The success of Internet social networking sites depends on the number and activity levels of their user members. Although users typically have numerous connections to other site members (i.e., “friends”), only a fraction of those so-called friends may actually influence a member's site usage. Because the influence of potentially hundreds of friends needs to be evaluated for each user, inferring precisely who is influential—and, therefore, of managerial interest for advertising targeting and retention efforts—is difficult. The authors develop an approach to determine which users have significant effects on the activities of others using the longitudinal records of members' log-in activity. They propose a nonstandard form of Bayesian shrinkage implemented in a Poisson regression. Instead of shrinking across panelists, strength is pooled across variables within the model for each user. The approach identifies the specific users who most influence others' activity and does so considerably better than simpler alternatives. For the social networking site data, the authors find that, on average, approximately one-fifth of a user's friends actually influence his or her activity level on the site.

Journal

Journal of Marketing ResearchSAGE

Published: Aug 1, 2010

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