Emergent user behavior on Twitter modelled by a stochastic differential equation

TY - JOUR

T1 - Emergent user behavior on Twitter modelled by a stochastic differential equation

AU - Møllgaard, Anders Edsberg

AU - Mathiesen, Joachim

PY - 2015/5/1

Y1 - 2015/5/1

N2 - Data from the social-media site, Twitter, is used to study the fluctuations in tweet rates of brand names. The tweet rates are the result of a strongly correlated user behavior, which leads to bursty collective dynamics with a characteristic 1/f noise. Here we use the aggregated "user interest" in a brand name to model collective human dynamics by a stochastic differential equation with multiplicative noise. The model is supported by a detailed analysis of the tweet rate fluctuations and it reproduces both the exact bursty dynamics found in the data and the 1/f noise.

AB - Data from the social-media site, Twitter, is used to study the fluctuations in tweet rates of brand names. The tweet rates are the result of a strongly correlated user behavior, which leads to bursty collective dynamics with a characteristic 1/f noise. Here we use the aggregated "user interest" in a brand name to model collective human dynamics by a stochastic differential equation with multiplicative noise. The model is supported by a detailed analysis of the tweet rate fluctuations and it reproduces both the exact bursty dynamics found in the data and the 1/f noise.

U2 - 10.1371/journal.pone.0123876

DO - 10.1371/journal.pone.0123876

M3 - Journal article

C2 - 25955783

SN - 1932-6203

VL - 10

JO - PLoS Computational Biology

JF - PLoS Computational Biology

IS - 5

M1 - e0123876

ER -