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

TL;DR

This paper applies neuronal spike train analysis techniques to human communication data, revealing distinct temporal dynamics and input-output response patterns across messaging, calling, and emailing channels.

Contribution

It introduces a novel application of spike train analysis to social communication, characterizing temporal fluctuations and modeling response behaviors.

Findings

Positive LV correlation in short messages

Different response time scales across channels

Model shows quick responses and refractory effects are key

Abstract

We study the dynamical properties of human communication through different channels, i.e., short messages, phone calls, and emails, adopting techniques from neuronal spike train analysis in order to characterize the temporal fluctuations of successive inter-event times. We first measure the so-called local variation (LV) of incoming and outgoing event sequences of users, and find that these in- and out- LV values are positively correlated for short messages, and uncorrelated for phone calls and emails. Second, we analyze the response-time distribution after receiving a message to focus on the input-output relationship in each of these channels. We find that the time scales and amplitudes of response are different between the three channels. To understand the impacts of the response-time distribution on the correlations between the LV values, we develop a point process model whose activity rate is modulated by incoming and outgoing events. Numerical simulations of the model indicate that a quick response to incoming events and a refractory effect after outgoing events are key factors to reproduce the positive LV correlations.