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.
Chandy and Gu proposed an approach to automatically identify spam reviews in the iOS App Store REF , which compared the performance of a baseline Decision Tree model with a novel Latent Class graphical model to the classification results of App review spam.
Identifying spam in the iOS app store
WebQuality '12
Computer Science