  In this paper, we propose a methodology quantifying temporal patterns of
nonlinear hashtag time series. Our approach is based on an analogy between
neuron spikes and hashtag diffusion. We adopt the local variation, originally
developed to analyze local time delays in neuron spike trains. We show that the
local variation successfully characterizes nonlinear features of hashtag spike
trains such as burstiness and regularity. We apply this understanding in an
extreme social event and are able to observe temporal evaluation of online
collective attention of Twitter users to that event.
