Quantitatively modelling opinion dynamics during elections

Political advertising has become overwhelmingly focused on online platforms, with ever-improving capabilities of tailored and targeted advertising to individuals. Modelling the effect of political propaganda on a voting population has become more prevalent in opinion dynamics research, which has become further enabled by the applications of computer simulation and data analysis. In this study we explore the effect of propaganda on a voting population. The agent-based model describes a population of voter agents who hold a political opinion using knowledge and emotion as their control variables. These variables are updated through agent interactions and political propaganda. The model is based on the emotion/information/opinion (E/I/O) approach which is applied to a grid-based and network population. Furthermore, the network is programmed to be partially dynamic, in that connections between disagreeing agents can be severed under certain conditions concerning the intimacy of agent relationships. This is performed with the intention of adding more flexibility to the model, whilst making it a more realistic representation of reality. The different network types are shown to produce varying proportions of metastable agent states from identical starting conditions, which can be used to represent real political situations and predict future change.