Calibration of predictive agent-based models of opinion dynamics

Opinion dynamics is a fast-growing interdisciplinary research area where problems of fundamental importance to our societies are studied with a mix of data-driven approaches and mathematical modelling. This PhD aims to develop quantitative approaches aimed at modelling and contrasting the diffusion of "fake news" and misinformation in online social networks (OSNs), which have been the subject of major research efforts in recent years. As the literature on the subject has grown, an apparent gap has emerged between its two main branches. On the one hand, increasingly sophisticated mathematical models have been put forward to understand how different rules of communication between individuals may lead to very different states (e.g., consensus, polarisation) in networked populations. On the other hand, data-driven studies have revealed several stylised facts that characterise the diffusion of information in the most popular OSNs, such as Facebook and Twitter. Yet, there is very little overlap between these two strands of research, as the currently available mathematical models struggle to come up with testable predictions, which in turn prevents from any meaningful model validation/rejection against empirical data. The focus of this PhD will be that of filling this gap by leveraging agent-based modelling and the wide array of existing techniques to calibrate agent-based models (ABMs) on empirical data.