Temporal Pattern of Online Communication Spike Trains in Spreading a Scientific Rumor: How Often, Who Interacts with Whom?

TL;DR

This study analyzes the temporal patterns of online communication during the spread of a scientific rumor on Twitter, revealing bursty activity in active users and Poisson-like behavior in popular passive users, with correlated dynamics in retweets and mentions.

Contribution

It introduces a detailed analysis of spike train dynamics in online social interactions, highlighting differences between active and passive users and their correlation patterns.

Findings

Active users exhibit bursty communication patterns.

Popular passive users show Poisson-like, uncorrelated activity.

Retweets and mentions are highly correlated for popular users.

Abstract

We study complex time series (spike trains) of online user communication while spreading messages about the discovery of the Higgs boson in Twitter. We focus on online social interactions among users such as retweet, mention, and reply, and construct different types of active (performing an action) and passive (receiving an action) spike trains for each user. The spike trains are analyzed by means of local variation, to quantify the temporal behavior of active and passive users, as a function of their activity and popularity. We show that the active spike trains are bursty, independently of their activation frequency. For passive spike trains, in contrast, the local variation of popular users presents uncorrelated (Poisson random) dynamics. We further characterize the correlations of the local variation in different interactions. We obtain high values of correlation, and thus consistent temporal behavior, between retweets and mentions, but only for popular users, indicating that creating online attention suggests an alignment in the dynamics of the two interactions.