Polarized Ukraine 2014: opinion and territorial split demonstrated with the bounded confidence XY model, parametrized by Twitter data

Abstract

Multiple countries have recently experienced extreme political polarization, which, in some cases, led to escalation of hate crime, violence and political instability. Besides the much discussed presidential elections in the USA 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 modelling of opinion dynamics, targeting Ukraine's crisis of 2014. We use Twitter data to quantify changes in the opinion divide and parametrize an extended bounded confidence XY model, which provides a spatio-temporal 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 behaviour 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.

Knowledge through social networks: Accuracy, error, and polarisation

“…This is not just a bold claim. There is evidence for the impact of group polarisation on everything from risky decisions [ 29 ], through judicial panels [ 30 ], to the rise of ethnic tensions and war (e.g., [ 31 , 32 ]).…”

There can be only one truth: Ideological segregation and online news communities in Ukraine

“…Even in the case of the US, where the degree of partisanship is high, the society is less ideologically segregated than in many non-Western countries, which suffer from extreme political polarisation. Romenskyy et al (2018) note that in these non-Western contexts, ideological segregation has a significant detrimental effect on the public sphere, leading to the escalation of hate crimes, political instability and violence; in some cases, it can also result in armed conflicts, such as the one currently occurring in Eastern Ukraine. Under such extreme conditions, not only is the impact of ideological segregation more visible than in the case of democratic systems, but the necessity to deal with it is also significantly more pressing as the persistence of segregated communities can lead to the aggravation of hostilities and undermine conflict resolution efforts (Hoffmann, 2014;Lynch et al, 2016).…”

Do social media create revolutions? Using Twitter sentiment analysis for predicting the Maidan Revolution in Ukraine

“…For example, Etling (2014) analysed attitudes towards the Euromaidan protests as expressed in tweets in various languages. In turn, Romenskyy et al (2018) studied Ukraine’s political polarization across the East and the West, and also analyzed Twitter data. Their research reflected on the polarization of opinion caused by the war in Donbass that had already begun at the time of the analysis.…”