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We study the dynamical properties of human communication through differentchannels, i.e., short messages, phone calls, and emails, adopting techniquesfrom neuronal spike train analysis in order to characterize the temporalfluctuations of successive inter-event times. We first measure the so-calledlocal variation (LV) of incoming and outgoing event sequences of users, andfind that these in- and out- LV values are positively correlated for shortmessages, and uncorrelated for phone calls and emails. Second, we analyze theresponse-time distribution after receiving a message to focus on theinput-output relationship in each of these channels. We find that the timescales and amplitudes of response are different between the three channels. Tounderstand the impacts of the response-time distribution on the correlationsbetween the LV values, we develop a point process model whose activity rate ismodulated by incoming and outgoing events. Numerical simulations of the modelindicate that a quick response to incoming events and a refractory effect afteroutgoing events are key factors to reproduce the positive LV correlations.

Input-output relationship in social communications characterized by spike train analysis