Human activity in the web.

The recent information technology revolution has enabled the analysis and processing of large-scale data sets describing human activities. The main source of data is represented by the web, where humans generally use to spend a relevant part of their day. Here, we study three large data sets containing the information about web activities of humans in different contexts. We study in details interevent and waiting-time statistics. In both cases, the number of subsequent operations which differs by tau units of time decays powerlike as tau increases. We use nonparametric statistical tests in order to estimate the significance level of reliability of global distributions to describe activity patterns of single users. Global interevent time probability distributions are not representative for the behavior of single users: the shape of single users' interevent distributions is strongly influenced by the total number of operations performed by the users and distributions of the total number of operations performed by users are heterogeneous. A universal behavior can be anyway found by suppressing the intrinsic dependence of the global probability distribution on the activity of the users. This suppression can be performed by simply dividing the interevent times with their average values. Differently, waiting-time probability distributions seem to be independent of the activity of users and global probability distributions are able to significantly represent the replying activity patterns of single users.