Understanding Human Dynamics of Check-in Behavior in LBSNs

With the increase of popularity and pervasive use of sensor-embedded smart phones, location-based social network services (LBSNs) are widely used in recent years. In this paper, we investigate human dynamics of the check-in data crawled from Jie Pang, a famous Chinese LBSN service. We study interval time and jump size (i.e. distance) between consecutive check-ins at both population level and individual level. We find out that both the interval time and jump size follow a Weibull distribution rather than a power law distribution at the population level. As for individual level for the top 10, 000 most active users, we find out that on one hand 9406 individuals follow a power law distribution and only 594 individuals follow a Weibull distribution in interval time distribution. On the other hand, 5096 individuals follow a Weibull distribution and 4904 individuals follow a power law distribution in jump size distribution. In addition, human check-in behavior from different gender and different cities are analyzed. Our experimental results show that users in Shanghai are more active than the users from other cities and females are more active than males in terms of check-in service.

[1]  Gregor Schiele,et al.  Understanding social relationship evolution by using real-world sensing data , 2012, World Wide Web.

[2]  Lei Gao,et al.  Individual and Group Dynamics in Purchasing Activity , 2010, ArXiv.

[3]  T. Geisel,et al.  Forecast and control of epidemics in a globalized world. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[4]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[5]  A. Barabasi,et al.  Dynamics of information access on the web. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[6]  Cecilia Mascolo,et al.  An Empirical Study of Geographic User Activity Patterns in Foursquare , 2011, ICWSM.

[7]  Daqing Zhang,et al.  The Emergence of Social and Community Intelligence , 2011, Computer.

[8]  Yihong Yuan,et al.  Analyzing and geo-visualizing individual human mobility patterns using mobile call records , 2010, 2010 18th International Conference on Geoinformatics.

[9]  Ryuichi Kitamura,et al.  Micro-simulation of daily activity-travel patterns for travel demand forecasting , 2000 .

[10]  Aravind Srinivasan,et al.  Modelling disease outbreaks in realistic urban social networks , 2004, Nature.

[11]  Alessandro Vespignani,et al.  Modeling the Worldwide Spread of Pandemic Influenza: Baseline Case and Containment Interventions , 2007, PLoS medicine.

[12]  Harry Eugene Stanley,et al.  Calling patterns in human communication dynamics , 2013, Proceedings of the National Academy of Sciences.

[13]  Saleem N. Bhatti,et al.  Modelling user behaviour in networked games , 2001, MULTIMEDIA '01.

[14]  H. Stanley,et al.  Modelling urban growth patterns , 1995, Nature.

[15]  Albert-László Barabási,et al.  Modeling bursts and heavy tails in human dynamics , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.