Robust dynamic classes revealed by measuring the response function of a social system

We study the relaxation response of a social system after endogenous and exogenous bursts of activity using the time series of daily views for nearly 5 million videos on YouTube. We find that most activity can be described accurately as a Poisson process. However, we also find hundreds of thousands of examples in which a burst of activity is followed by an ubiquitous power-law relaxation governing the timing of views. We find that these relaxation exponents cluster into three distinct classes and allow for the classification of collective human dynamics. This is consistent with an epidemic model on a social network containing two ingredients: a power-law distribution of waiting times between cause and action and an epidemic cascade of actions becoming the cause of future actions. This model is a conceptual extension of the fluctuation-dissipation theorem to social systems [Ruelle, D (2004) Phys Today 57:48–53] and [Roehner BM, et al., (2004) Int J Mod Phys C 15:809–834], and provides a unique framework for the investigation of timing in complex systems.

[1]  A. Johansen Response time of internauts , 2001, cond-mat/0103022.

[2]  H. Kantz,et al.  Extreme Events in Nature and Society , 2006 .

[3]  Jan Beran,et al.  Statistics for long-memory processes , 1994 .

[4]  D. Sornette,et al.  Response Functions to Critical Shocks in Social Sciences , 2004, cond-mat/0402408.

[5]  M. Westoby,et al.  Bivariate line‐fitting methods for allometry , 2006, Biological reviews of the Cambridge Philosophical Society.

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

[7]  D. Sornette,et al.  Endogenous Versus Exogenous Shocks in Complex Networks: An Empirical Test Using Book Sale Rankings , 2003, Physical review letters.

[8]  A. Hawkes,et al.  A cluster process representation of a self-exciting process , 1974, Journal of Applied Probability.

[9]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

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

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

[12]  David Ruelle,et al.  Conversations on nonequilibrium physics with an extraterrestrial , 2004 .

[13]  Didier Sornette,et al.  Download relaxation dynamics on the WWW following newspaper publication of URL , 2000 .

[14]  D. Sornette,et al.  Endogenous versus exogenous shocks in systems with memory , 2002, cond-mat/0206047.

[15]  T Pollmann,et al.  On forgetting the historical past , 1998, Memory & cognition.

[16]  Chris Anderson,et al.  The Long Tail: Why the Future of Business is Selling Less of More , 2006 .

[17]  A. Vazquez,et al.  Impact of interactions on human dynamics , 2007, 0710.4916.

[18]  D. Sornette,et al.  Dynamics of book sales: endogenous versus exogenous shocks in complex networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[19]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

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

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