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

## TL;DR

This paper combines social media analysis and agent-based modeling to understand Ukraine's 2014 polarization, revealing emotional intensity as a key factor driving sudden shifts in opinion and collective behavior.

## Contribution

It introduces a novel approach by parameterizing an extended Bounded-Confidence XY Model with Twitter data to analyze polarization dynamics in Ukraine.

## Key findings

- Emotional intensity significantly influences polarization transitions.
- A critical emotional intensity level triggers a sudden increase in opinion bimodality.
- The model captures spatiotemporal polarization patterns during Ukraine's crisis.

## Abstract

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