title: Complex contagion process in spreading of online innovation.
abstract: Diffusion of innovation can be interpreted as a social spreading phenomenon governed by the impact of media and social interactions. Although these mechanisms have been identified by quantitative theories, their role and relative importance are not entirely understood, as empirical verification has so far been hindered by the lack of appropriate data. Here we analyse a dataset recording the spreading dynamics of the world's largest Voice over Internet Protocol service to empirically support the assumptions behind models of social contagion. We show that the rate of spontaneous service adoption is constant, the probability of adoption via social influence is linearly proportional to the fraction of adopting neighbours, and the rate of service termination is time-invariant and independent of the behaviour of peers. By implementing the detected diffusion mechanisms into a dynamical agent-based model, we are able to emulate the adoption dynamics of the service in several countries worldwide. This approach enables us to make medium-term predictions of service adoption and disclose dependencies between the dynamics of innovation spreading and the socio-economic development of a country.
authors.affiliation: Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, MA 02115, USA Department of Biomedical Engineering and Computational Science (BECS), Aalto University School of Science, 00076 Aalto, Finland Software Technology and Applications Competence Center (STACC), 51003 Tartu, Estonia Laboratoire de l'Informatique du Parallélisme, INRIA-UMR 5668, IXXI, ENS de Lyon, 69364 Lyon, France marton.karsai@ens-lyon.fr.
authors.forename: Márton
authors.initials: M
authors.lastname: Karsai
authors.affiliation: Department of Biomedical Engineering and Computational Science (BECS), Aalto University School of Science, 00076 Aalto, Finland.
authors.forename: Gerardo
authors.initials: G
authors.lastname: Iñiguez
authors.affiliation: Department of Biomedical Engineering and Computational Science (BECS), Aalto University School of Science, 00076 Aalto, Finland CABDyN Complexity Centre, Säid Business School, University of Oxford, Oxford OX1 1HP, UK.
authors.forename: Kimmo
authors.initials: K
authors.lastname: Kaski
authors.affiliation: Department of Biomedical Engineering and Computational Science (BECS), Aalto University School of Science, 00076 Aalto, Finland Center for Network Science, Central European University, 1051 Budapest, Hungary Institute of Physics, Budapest University of Technology and Economics, 1111 Budapest, Hungary.
authors.forename: János
authors.initials: J
authors.lastname: Kertész
mesh_terms: D006801:Humans; D048088:Informatics; D020407:Internet; D008962:Models, Theoretical
keywords: complex contagion phenomena; data-driven modelling; mean-field approximation