Disclosure Of Fraud Ranking In Mobile App Using Relationship Among Rating Review And Ranking
Ranking fraud in the mobile App market refers to fraudulent or deceptive activities which have a purpose of
bumping up the Apps in the popularity list. Indeed, it becomes more and more frequent for App developers to use shady
means, such as inflating their Apps’ sales or posting phony App ratings, to commit ranking fraud. While the importance of
preventing ranking fraud has been widely recognized, there is limited understanding and research in this area. To this end, in
this paper, we provide a holistic view of ranking fraud and propose a ranking fraud detection system for mobile Apps.
Specifically, we first propose to accurately locate the ranking fraud by mining the active periods, namely leading sessions, of
mobile Apps. Such leading sessions can be leveraged for detecting the local anomaly instead of global anomaly of App
rankings. Furthermore, we investigate three types of evidences, i.e., ranking based evidences, rating based evidences and
review based evidences, by modeling Apps’ ranking, rating and review behaviors through statistical hypotheses tests. In
addition, we propose an optimization based aggregation method to integrate all the evidences for fraud detection. Finally, we
evaluate the proposed system with real-world App data collected from the iOS App Store for a long time period. In the
experiments, we validate the effectiveness of the proposed system, and show the scalability of the detection algorithm as
well as some regularity of ranking fraud activities.
Index Terms—Mobile Apps, ranking fraud detection, evidence aggregation, historical ranking records, rating and review