Gaining a better understanding of online polarization by approaching it as a dynamic process

TL;DR

This study models online polarization as a dynamic, evolving process influenced by debate maturity and context, using Twitter data from French populations on COVID-19 and Ukraine conflicts.

Contribution

It introduces a novel approach to analyze polarization as a temporal process, comparing mature and emerging debates across different contexts and populations.

Findings

Polarization varies over time with specific periods.

Debate maturity influences polarization dynamics.

Context-related events pace polarization changes.

Abstract

Polarization is often a clich{\'e}, its conceptualization remains approximate and no consensus has been reached so far. Often simply seen as an inevitable result of the use of social networks, polarization nevertheless remains a complex social phenomenon that must be placed in a wider context. To contribute to a better understanding of polarization, we approach it as an evolving process, drawing on a dual expertise in political and data sciences. We compare the polarization process between one mature debate (COVID-19 vaccine) and one emerging debate (Ukraine conflict) at the time of data collection. Both debates are studied on Twitter users, a highly politicized population, and on the French population to provide key elements beyond the traditional US context. This unprecedented analysis confirms that polarization varies over time, through a succession of specific periods, whose existence and duration depend on the maturity of the debate. Importantly, we highlight that polarization is paced by context-related events. Bearing this in mind, we pave the way for a new generation of personalized depolarization strategies, adapted to the context and maturity of debates.