An empirical approach for fake user detection in location-based social networks

Location-based social networks are becoming a unique platform for understanding user behaviors and providing pervasive services in intelligent environments. However, fake users or accounts can undermine user analytics and lower the value of the applications and services intended for real users. Mining a large Foursquare dataset and related Twitter accounts, we tested different user features with the goal of classifying fake users. Experiments demonstrate an accuracy over 95% in detecting fake users. Filtering out these fake users reduces the error rate of a location-based activity predictor by a 4.4% and avoids wasting 35% of coupons or promotion codes delivery if applied to a recommender system.