Mathematical models of social contagion that incorporate networks of human interactions have become increasingly popular, however, very few approaches have tackled the challenges of including complex and realistic properties of socio-technical systems. Here, we define a framework to characterize the dynamics of the Maki–Thompson rumour spreading model in structured populations, and analytically find a previously uncharacterized dynamical phase transition that separates the local and global contagion regimes. We validate our threshold prediction through extensive Monte Carlo simulations. Furthermore, we apply this framework in two real-world systems, the European commuting and transportation network and the Digital Bibliography and Library Project collaboration network. Our findings highlight the importance of the underlying population structure in understanding social contagion phenomena and have the potential to define new intervention strategies aimed at hindering or facilitating the diffusion of information in socio-technical systems.

包含人类互动网络的社会传染数学模型变得越来越流行，然而，很少有方法能够解决包括社会技术系统的复杂和现实特性的挑战。在这里，我们定义了一个框架来表征结构化人群中 Maki-Thompson 谣言传播模型的动态，并通过分析找到一个以前未表征的动态相变，它将局部和全球传染机制分开。我们通过广泛的蒙特卡罗模拟验证了我们的阈值预测。此外，我们将该框架应用于两个现实世界的系统，即欧洲通勤和交通网络以及数字书目和图书馆项目协作网络。