A Hierarchy of Linear Threshold Models for the Spread of Political Revolutions on Social Networks

We study a linear threshold agent-based model (ABM) for the spread of political revolutions on social networks using empirical network data. We propose new techniques for building a hierarchy of simplified ordinary differential equation (ODE) based models that aim to capture essential features of the ABM, including effects of the actual networks, and give insight in the parameter regime transitions of the ABM. We relate the ABM and the hierarchy of models to a population-level compartmental ODE model that we proposed previously for the spread of political revolutions [1], which is shown to be mathematically consistent with the proposed ABM and provides a way to analyze the global behaviour of the ABM. This consistency with the linear threshold ABM also provides further justification a posteriori for the compartmental model of [1]. Extending concepts from epidemiological modelling, we define a basic reproduction number $R_0$ for the linear threshold ABM and apply it to predict ABM behaviour on empirical networks. In small-scale numerical tests we investigate experimentally the differences in spreading behaviour that occur under the linear threshold ABM model when applied to some empirical online and offline social networks, searching for quantitative evidence that political revolutions may be facilitated by the modern online social networks of social media.