Complex contagion process in spreading of online innovation

TL;DR

This paper empirically analyzes the spread of online innovation through social contagion, revealing key mechanisms and enabling medium-term adoption predictions across countries.

Contribution

It provides empirical validation of social contagion models for online innovation diffusion and introduces an agent-based model for accurate medium-term adoption forecasting.

Findings

Adoption rate due to social influence is linearly proportional to adopting neighbors.

Spontaneous adoption rate remains constant over time.

Service termination rate is time-invariant and unaffected by peers.

Abstract

Diffusion of innovation can be interpreted as a social spreading phenomena 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, since 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 socioeconomic development of a country.