In a more and more polarized world, the role of underling social structure driving social influence in opinions and behaviours is still unexplored. In this study we developed a method, the kernel-Blau-Ising model (KBI), to uncover how people are influenced/connected based on their socio-demographic coordinates (e.g., income, age, education, postcode), and tested the model in the EU referendum and two London Mayoral elections.
Despite using no social network data, we discover established signatures of homophily, the tendency to befriend those similar to oneself—the stronger homophily is, the more social segregation. We found consistent geographical segregation for the three elections, while education was a strong segregation factor for the EU Referendum, it wasn’t for Mayoral Elections, however, age and income were. The model can be used to explore how reducing inequalities or encouraging mixing among groups can reduce social polarization. You can read about our work "Inference of a universal social scale and segregation measures using social connectivity kernels" free in the journal Science Advances here. Antonia and Nick