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We study complex time series (spike trains) of online user communicationwhile spreading messages about the discovery of the Higgs boson in Twitter. Wefocus on online social interactions among users such as retweet, mention, andreply, and construct different types of active (performing an action) andpassive (receiving an action) spike trains for each user. The spike trains areanalyzed by means of local variation, to quantify the temporal behavior ofactive and passive users, as a function of their activity and popularity. Weshow that the active spike trains are bursty, independently of their activationfrequency. For passive spike trains, in contrast, the local variation ofpopular users presents uncorrelated (Poisson random) dynamics. We furthercharacterize 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 thatcreating online attention suggests an alignment in the dynamics of the twointeractions.

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