Communication activity in a social network: relation between long-term correlations and inter-event clustering

TL;DR

This paper investigates the collective communication patterns in social networks, revealing that while individual activity correlations stem from inter-event clustering, community-wide long-term correlations emerge as a systemic property.

Contribution

It distinguishes between individual and collective activity correlations, showing that community-level long-term correlations are emergent and not due to individual inter-event time distributions.

Findings

Individual activity correlations are due to inter-event clustering.

Community activity exhibits true long-term correlations as an emergent property.

Long-term correlations are not explained by inter-event time distributions.

Abstract

The timing patterns of human communication in social networks is not random. On the contrary, communication is dominated by emergent statistical laws such as non-trivial correlations and clustering. Recently, we found long-term correlations in the user's activity in social communities. Here, we extend this work to study collective behavior of the whole community. The goal is to understand the origin of clustering and long-term persistence. At the individual level, we find that the correlations in activity are a byproduct of the clustering expressed in the power-law distribution of inter-event times of single users. On the contrary, the activity of the whole community presents long-term correlations that are a true emergent property of the system, i.e. they are not related to the distribution of inter-event times. This result suggests the existence of collective behavior, possible arising from nontrivial communication patterns through the embedding social network.