Human Activity in the Web

TL;DR

This paper analyzes large-scale Web activity datasets, revealing universal patterns in inter-event and waiting times, and demonstrating that normalized inter-event times can represent individual user behavior.

Contribution

It introduces a method to normalize inter-event times to uncover universal activity patterns across users on the Web.

Findings

Inter-event times decay power-like with tau.

Global distributions do not represent individual behavior.

Normalized inter-event times reveal universal patterns.

Abstract

The recent information technology revolution has enabled the analysis and processing of large-scale datasets 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 datasets containing the information about Web human activities in different contexts. We study in details inter-event and waiting time statistics. In both cases, the number of subsequent operations which differ by tau units of time decays power-like as tau increases. We use non-parametric statistical tests in order to estimate the significance level of reliability of global distributions to describe activity patterns of single users. Global inter-event time probability distributions are not representative for the behavior of single users: the shape of single users'inter-event 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 inter-event 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.