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Multiple countries have recently experienced extreme political polarization,which in some cases led to escalation of hate crime, violence and politicalinstability. Beside the much discussed presidential elections in the UnitedStates and France, Britain's Brexit vote and Turkish constitutional referendum,showed signs of extreme polarization. Among the countries affected, Ukrainefaced some of the gravest consequences. In an attempt to understand themechanisms of these phenomena, we here combine social media analysis withagent-based modeling of opinion dynamics, targeting Ukraine's crisis of 2014.We use Twitter data to quantify changes in the opinion divide and parameterizean extended Bounded-Confidence XY Model, which provides a spatiotemporaldescription of the polarization dynamics. We demonstrate that the level ofemotional intensity is a major driving force for polarization that can lead toa spontaneous onset of collective behavior at a certain degree of homophily andconformity. We find that the critical level of emotional intensity correspondsto a polarization transition, marked by a sudden increase in the degree ofinvolvement and in the opinion bimodality.

Polarized Ukraine 2014: Opinion and Territorial Split Demonstrated with the Bounded Confidence XY Model, Parameterized by Twitter Data