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This study examines social influence among a subset of users on Yelp.com through network centrality scores based on users’ communicative exchanges in reviews. Drawing from contagion theory, eigenvector centralities were measured to assess the level of influence people had within a social network. A social network was created to illuminate how elite and non-elite status contributed to how central users were in the network. Through negative bi-nominal regression, results indicate lower numbers of reviews a user gave within the network predicted their eigenvector centrality. Furthermore, depending on location and market size of a business, elite status, and critical reviews were significant predictors for eigenvector centrality in smaller, localized cities. These findings highlight how specific node attributes can predict social influence within the network. This study details how users on Yelp prefer the quality of reviews over the quantity of them. Implications for future social network research are also discussed.
Ignacio Cruz, Annenberg School for Communication and Journalism, U of Southern California
Jillian Kwong, U of Southern California