Community-based cheater detection in location-based social networks

Location-based social networks provide services that allow users to share their locations with friends. To attract users and keep them active, social networks or venue holders may offer some awards. But some users make fake check-ins to achieve these awards. These cheaters cause monetary loss and decrease the accuracy of venue recommendations. In this paper, we study users of Foursquare, a popular location-based social network. Behaviors of cheaters and normal users are discussed. Two types of connections are defined to construct graphs of these users. And we propose a method to find cheaters using community structure of the constructed graphs. Our results verify that this cheater detection method is effective and costs little.