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

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.