Modeling the adoption of innovations in the presence of geographic and media influences

TL;DR

This paper develops a geographically and media-aware social contagion model to better predict city-level innovation adoption, demonstrating mass media's significant role in accelerating Twitter's user growth.

Contribution

It introduces a novel contagion model incorporating real geography and endogenous mass media influence, improving prediction accuracy for innovation adoption.

Findings

Geography and homophily are crucial for realistic adoption modeling.

Mass media increased Twitter's user base two to four times.

Extended contagion models to include responsive mass media influence.

Abstract

While there has been much work examining the affects of social network structure on innovation adoption, models to date have lacked important features such as meta-populations reflecting real geography or influence from mass media forces. In this article, we show these are features crucial to producing more accurate predictions of a social contagion and technology adoption at the city level. Using data from the adoption of the popular micro-blogging platform, Twitter, we present a model of adoption on a network that places friendships in real geographic space and exposes individuals to mass media influence. We show that homopholy both amongst individuals with similar propensities to adopt a technology and geographic location are critical to reproduce features of real spatiotemporal adoption. Furthermore, we estimate that mass media was responsible for increasing Twitter's user base two to four fold. To reflect this strength, we extend traditional contagion models to include an endogenous mass media agent that responds to those adopting an innovation as well as influencing agents to adopt themselves.