Modeling correlated human dynamics

TL;DR

This paper investigates human blogging activity patterns, revealing strong memory effects and heavy-tailed inter-event times, and proposes a new model based on temporal preference to better explain these phenomena.

Contribution

It introduces a simple temporal preference model that captures memory effects and activity-dependent distributions, improving upon prior priority-queue models.

Findings

Memory coefficient decays as a power law then exponentially.

Inter-event times follow a heavy-tailed distribution with activity-dependent exponents.

The proposed model reproduces observed memory and distribution patterns.

Abstract

We empirically study the activity patterns of individual blog-posting and find significant memory effects. The memory coefficient first decays in a power law and then turns to an exponential form. Moreover, the inter-event time distribution displays a heavy-tailed nature with power-law exponent dependent on the activity. Our findings challenge the priority-queue model that can not reproduce the memory effects or the activity-dependent distributions. We think there is another kind of human activity patterns driven by personal interests and characterized by strong memory effects. Accordingly, we propose a simple model based on temporal preference, which can well reproduce both the heavy-tailed nature and the strong memory effects. This work helps in understanding both the temporal regularities and the predictability of human behaviors.