Distance Measures for Detecting Geo-Social Similarity

This paper investigates the problem of geo-social similarity among users of online social networks, based on the locations of their activities (e.g., posting messages or photographs). Finding pairs of geo-socially similar users or detecting that two sets of locations (of activities) belong to the same user has important applications in privacy protection, recommendation systems, urban planning, public health, etc. It is explained and shown empirically that common distance measures between sets of locations are inadequate for determining geo-social similarity. Two novel distance measures between sets of locations are introduced. One is the mutually nearest distance that is based on computing a matching between two sets. The second measure uses a quad-tree index. It is highly scalable, but incurs the overhead of creating and maintaining the index. Algorithms with optimization techniques are developed for computing the two distance measures and also for finding the k-most similar users of a given one. Extensive experiments, using geo-tagged messages from Twitter, show that the new distance measures are both more accurate and more efficient than existing ones.