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Abstract
Human activity plays a central role in understanding large-scale social dynamics. It is well documented that individual activity pattern follows bursty dynamics characterized by heavy-tailed interevent time distributions. Here we study a large-scale online chatting dataset consisting of 5,549,570 users, finding that individual activity pattern varies with timescales whereas existing models only approximate empirical observations within a limited timescale. We propose a novel approach that models the intensity rate of an individual triggering an activity. We demonstrate that the model precisely captures corresponding human dynamics across multiple timescales over five orders of magnitudes. Our model also allows extracting the population heterogeneity of activity patterns, characterized by a set of individual-specific ingredients. Integrating our approach with social interactions leads to a wide range of implications.
Highlights
Human activity pattern is one of the central building blocks of modeling and understanding social dynamics such as information spreading [1,2,3], social-tie and group formations [4,5,6,7], social cooperations and competitions [8, 9]
Previous studies have shown that human dynamics is characterized by bursts of events and long periods of inactivity
Our study of high resolution records of human interactive behavior provides an in-depth analysis of human dynamics, revealing non-Poisson temporal patterns that suggests a rethinking of mechanisms governing the human dynamics
Summary
Human activity pattern is one of the central building blocks of modeling and understanding social dynamics such as information spreading [1,2,3], social-tie and group formations [4,5,6,7], social cooperations and competitions [8, 9]. Recent researches on human dynamics have demonstrated extensive evidence [10,11,12] that the interevent time (time between consecutive messages) and the response time (time between a message was received and the reply was sent) τ are heavy-tailed distributed, in contrast to prediction of the uncorrelated Poisson process where the interevent time distribution P(τ) follows an exponential form. This indicates that the vast majority of responses were sent within a very short time frame known as bursts. In social systems the “propagator” (interevent time distribution) P(τ) of a free “particle” (person) is fundamentally distinct from these in the physical science that are purely random
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