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Identification of influential social networkers

Identification of influential social networkers 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Web Based Communities Inderscience Publishers

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References (32)

Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1477-8394
eISSN
1741-8216
DOI
10.1504/IJWBC.2012.046256
Publisher site
See Article on Publisher Site

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.

Journal

International Journal of Web Based CommunitiesInderscience Publishers

Published: Jan 1, 2012

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