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

Abstract

Human communication in social networks is dominated by emergent statistical laws such as non-trivial correlations and temporal clustering. Recently, we found long-term correlations in the user's activity in social communities. Here, we extend this work to study the collective behavior of the whole community with the goal of understanding 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, i.e. short periods of many events are separated by long periods of no events. 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, possibly arising from nontrivial communication patterns through the embedding social network.