Document Type

Article

Publication Date

1-1-2012

Publication Title

International Journal of Web Based Communities

Volume

8

Issue Number

2

First Page

136

Last Page

158

DOI

10.1504/IJWBC.2012.046256

Abstract

Online social networking is deeply interleaved in today's lifestyle. People come together and build communities to share thoughts, offer suggestions, exchange information, ideas, and opinions. Moreover, social networks often serve as platforms for information dissemination and product placement or promotion through viral marketing. The success rate in this type of marketing could be increased by targeting specific individuals, called 'influential users', having the largest possible reach within an online community. In this paper, we present a method aiming at identifying the influential users within an online social networking application. We introduce ProfileRank, a metric that uses popularity and activity characteristics of each user to rank them in terms of their influence. We then assess this algorithm's added value in identifying influential users compared to other commonly used social network analysis metrics, such as the betweenness centrality and the well-known PageRank, by performing an experimental evaluation on a synthetic and a real-life dataset. We also integrate all three metrics in a unified metric and measure its performance.

Comments

Copyright © 2012 Inderscience Enterprises Ltd. This is the author's post-print verison of a work that was accepted for publication in the International Journal of Web Based Communities. DOI: 10.1504/IJWBC.2012.046256

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