Location-based social network users through a lense: Examining temporal user patterns

There has been a rapid proliferation of location-based social networks (LBSNs) during the last years. The spatial component of these systems provides a rich source of information that can be exploited by a number of novel services. However, to better design such services, it is important to understand the way people make use of these platforms and how this usage changes over time. While there exist studies that examine the motivations of people for adopting the usage of LBSNs and the temporal dynamics of these motivations, they are based on interviews and are mostly qualitative. Motivations can further only indirectly reveal or help us infer user behavior. In this paper, we analyze data from two commercial LBSNs to examine the temporal evolution of usage patterns to see what the data on their own reveal. We find that users of two social networks that we examined increase their level of activity as they use the system. However, depending on the main purpose of the underlying LBSN, users may exhibit different behaviors over time. We believe that our findings can open new directions and stimulate further research on areas such as location prediction and its applications (e.g., urban and transportation planning and location-based advertisment). Copyright © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved