Characterizing users' check-in activities using their scores in a location-based social network

Analysis of users' check-ins in location-based social networks (LBSNs, also called GeoSocial Networks), such as Foursquare and Yelp, is essential to understand users' mobility patterns and behaviors. However, most empirical results of users' mobility patterns reported in the current literature are based on users' sampled and nonconsecutive public check-ins. Additionally, such analyses take no account of the noise or false information in the dataset, such as dishonest check-ins created by users. These empirical results may be biased and hence may bring side effects to LBSN services, such as friend and venue recommendations. Foursquare, one of the most popular LBSNs, provides a feature called a user's score. A user's score is an aggregate measure computed by the system based on more accurate and complete check-ins of the user. It reflects a snapshot of the user's temporal and spatial patterns from his/her check-ins. For example, a high user score indicates that the user checked in at many venues regularly or s/he visited a number of new venues. In this paper, we show how a user's score can be used as an alternative way to investigate the user's mobility patterns. We first characterize a set of properties from the time series of a user's consecutive weekly scores. Based on these properties, we identify different types of users by clustering users' common check-in patterns using non-negative matrix factorization (NMF). We then analyze the correlations between the social features of user clusters and users' check-in patterns. We present several interesting findings. For example, users with high scores (more mobile) tend to have more friends (more social). Our empirical results demonstrate how to uncover interesting spatio-temporal patterns by utilizing the aggregate measures released by a LBSN service