Informing opinion dynamics models with online social network data

The literature contains a manifold of fundamental models for the dynamical changes of opinions through social influence during social interaction (personal, social networks). Although some of these models qualitatively reproduce real-world phenomena like the emergence of opinion polarization, or echo chambers, their assumptions and outcomes are still quite far from being empirically adequate since an appropriate data-based approach for empirical validation is still missing. To overcome this gap, we aim to build a model in close comparison to real-world data from the beginning. By extending traditional bounded confidence models of opinion dynamics, we propose a new mathematical model for complex co-evolution of opinion and social dynamics. Our model will rely on empirical, time-dependent social network data (from Twitter) that is coupled to a stochastic opinion dynamics model for a large, but finite number of interacting agents. Once the model’s outcomes of social- and individual-opinion correlations are validated against the respective measures from Twitter, it can be used to achieve a better understanding of how polarization and echo-chambers are created and to provide empirical underpinning on how people shape their opinions in their online social space to assess the risk of possible mechanism for manipulations by automated bots.