The Empirical Research And Model On On-Line Human Dynamic

The significance of the quantitative understanding of human behavior is quite obvious since the dynamics of many social, technological and economic phenomena are driven by individual human actions. Thanks to the development of the information technology, more and more electronic records available from internet give us a valuable insight into the pattern of human behaviors. From surface mail to short message, a wide variety of human activities were studied in recent years. The main result, arising from all these studies, concerns the heavy-tailed nature of human activity: the interevent times follows a power-law distribution both at the level of population and individual. Here, we study three large data sets containing the information about web activities of humans in different contexts: Blog-posting, Wiki-revising, Bookmarking. We study in details interevent statistics. In all cases, the distributions of the interevent ti meτdecay powerlik e asτincrease at both individual and population levels. Unlike previous studies, we put emphasis on time scales and obtain heterogeneous decay exponents in the intra- and inter-day range for the same dataset. Moreover, we observe opposite trend of exponents in relation to individual activity. In blog-posting, we found significant short-term correlation which is different from the previous results. Interestingly, when the time lag K is less than 10 the correlation coefficient decays as a power law and when K is more than 10 it decrease exponentially. In wiki-revising, investigations show that the presence of intra-day activities mask the correlation between consecutive inter-day activities and lead to an underestimate of memory, which explain the contradicting results above. Removal of data in intra-day range reveals the high values of memory and leads us to convergent results between wiki-revising and blog-posting. In bookmarking, Instead of monotonically increasing with activity, inter-day exponent peaks around 3. We further show that the global distributions of less active users are closer to exponential distribution than the ones of more active users. Moreover a universal behavior in the inter-day range is observed by considering the rescaled variable. In order to explain these observations, A simple model based on the personal preference was supposed by us. There are two main rules in this model: (1) the more the user performs an activity recently, the more likely he will do it next; (2) there exists occasions that users choose what to do randomly with independent probability. Different from the previous studies which only focused on the exponent, our model reproduced all these three key features: the heavy-tails, the strong short-term correlation, the dependence between the exponents and Activity. We also discussed the possible causes of the two regimes in the decay curve of correlation coefficient. Our findings may provide insight into not only the origin of heavy-tails but also the predictability in human behaviors.