A Location Spoofing Detection Method for Social Networks (Short Paper)

Abstract

It is well known that check-in data from location-based social networks (LBSN) can be used to predict human movement. However, there are large discrepancies between check-in data and actual user mobility, because users can easily spoof their location in LBSN. The act of location spoofing refers to intentionally making false location, leading to a negative impact both on the credibility of location-based social networks and the reliability of spatial-temporal data. In this paper, a location spoofing detection method in social networks is proposed. First, Latent Dirichlet Allocation (LDA) model is used to learn the topics of users by mining user-generated microblog information, based on this a similarity matrix associated with the venue is calculated. And the venue visiting probability is computed based on user historical check-in data by using Bayes model. Then, the similarity value and visiting probability is combined to quantize the probability of location spoofing. Experiments on a large scale and real-world LBSN dataset collected from Weibo show that the proposed approach can effectively detect certain types of location spoofing.