Modeling microblogging communication based on human dynamics

Microblog is a large-scale information sharing platform where intensive communications are taking place through interactive user behaviors. Previous studies have analyzed and modeled a series of traditional communications, such as letter, email and phone calls. The timing of human activities tends to be non-Poisson with bursts and heavy tails. Hence, several models have been proposed to explain human dynamics of bursts and heavy tails in various fields. However, as a newly developed product of Web2.0, microblog possesses inherent new characteristics, e.g. user-centric broadcast medium and asymmetric user relations. The communication in microblog is still poorly understood. Our work proposed an interest-driven model to simulate basic user communicating behaviors and processes. We came to the conclusion that, in microblogging communication, individual behaviors are bursts of rapidly occurring events separated by long periods of inactivity. Collective behaviors follow heavy-tailed Power Law distribution, whose origin is the descent of interests. Empirical statistics were also given to verify the model.

[1]  A. Barabasi,et al.  Human dynamics: Darwin and Einstein correspondence patterns , 2005, Nature.

[2]  G. Madey,et al.  Uncovering individual and collective human dynamics from mobile phone records , 2007, 0710.2939.

[3]  Tao Zhou,et al.  Empirical analysis on temporal statistics of human correspondence patterns , 2008 .

[4]  J. G. Oliveira,et al.  Human Dynamics: The Correspondence Patterns of Darwin and Einstein , 2005 .

[5]  Scott Counts,et al.  Predicting the Speed, Scale, and Range of Information Diffusion in Twitter , 2010, ICWSM.

[6]  M. Newman Power laws, Pareto distributions and Zipf's law , 2005 .

[7]  Alex Hai Wang,et al.  Don't follow me: Spam detection in Twitter , 2010, 2010 International Conference on Security and Cryptography (SECRYPT).

[8]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[9]  F. Haight Handbook of the Poisson Distribution , 1967 .

[10]  Bruno Gonçalves,et al.  Human dynamics revealed through Web analytics , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  Peng Wang,et al.  Modeling correlated human dynamics , 2010, ArXiv.

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

[13]  A.R.M. Teutle,et al.  Twitter: Network properties analysis , 2010, 2010 20th International Conference on Electronics Communications and Computers (CONIELECOMP).

[14]  Adilson E. Motter,et al.  A Poissonian explanation for heavy tails in e-mail communication , 2008, Proceedings of the National Academy of Sciences.

[15]  Tao Zhou,et al.  Modeling human dynamics with adaptive interest , 2007, 0711.0741.

[16]  J. K. Ord,et al.  Handbook of the Poisson Distribution , 1967 .

[17]  Albert-László Barabási,et al.  The origin of bursts and heavy tails in human dynamics , 2005, Nature.

[18]  P. Holme Network dynamics of ongoing social relationships , 2003, cond-mat/0308544.

[19]  L. Amaral,et al.  On Universality in Human Correspondence Activity , 2009, Science.

[20]  G Caldarelli,et al.  Invasion percolation and critical transient in the Barabási model of human dynamics. , 2007, Physical review letters.

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

[22]  Timothy W. Finin,et al.  Why we twitter: understanding microblogging usage and communities , 2007, WebKDD/SNA-KDD '07.

[23]  Alexei Vazquez Impact of memory on human dynamics , 2007 .