Identifying spam in the iOS app store

Authors:
Rishi Chandy;Haijie Gu
Affiliations:
Carnegie Mellon University;Carnegie Mellon University
Venue:
Proceedings of the 2nd Joint WICOW/AIRWeb Workshop on Web Quality
Year:
2012

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Abstract

Popular apps on the Apple iOS App Store can generate millions of dollars in profit and collect valuable personal user information. Fraudulent reviews could deceive users into downloading potentially harmful spam apps or unfairly ignoring apps that are victims of review spam. Thus, automatically identifying spam in the App Store is an important problem. This paper aims to introduce and characterize novel datasets acquired through crawling the iOS App Store, compare a baseline Decision Tree model with a novel Latent Class graphical model for classification of app spam, and analyze preliminary results for clustering reviews.