Determining Influential Users in Internet Social Networks

TLDR

Internet social networking sites rely on member numbers and activity, yet only a minority of a user's many connections actually influence their usage, making it hard to identify key influencers. The study proposes a method to identify users who significantly affect others' activity based on members' log‑in records. The method uses a Poisson regression with a nonstandard Bayesian shrinkage that pools influence strength across variables for each user. The approach accurately pinpoints influential users, outperforming simpler methods, and shows that roughly 20 % of a user's friends drive their activity.

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.