Crisis-induced differences in attention towards Ukraine in Twitter 2008-2023

TL;DR

This study analyzes Twitter attention towards Ukraine across 28 languages from 2008 to 2023, revealing distinct attention patterns linked to major conflicts and highlighting asymmetries in information access due to platform algorithms.

Contribution

It introduces a novel macro-scale cartographic method to compare multilingual attention patterns over time, uncovering language-specific clusters related to Ukraine crises.

Findings

Two main language clusters peaking around 2014 and 2022.

Distinct onset and decay profiles reflecting national support levels.

Asymmetry in data transparency between public access and platform algorithms.

Abstract

Aggression against Ukraine has drawn widespread international attention, particularly in the wake of the two Russian invasions into Ukrainian territory in 2014 and 2022. Although previous studies have examined social-media dynamics around these events, a comparative longitudinal data-driven view across languages is still missing. This article fills this gap by mapping added attention to "Ukraine" on Twitter in 28 languages from 2008 to 2023, using a deceptively simple DNA microarray-inspired cartography of log over-expression relative to each language's baseline frequency. This macro-scale visualization makes familiar events stand out while uncovering subtler patterns beyond the cognitive reach of any single-language audience. Most strikingly, two nearly non-overlapping language clusters emerge, one peaking around 2014 and the other around 2022 with distinct onset and decay profiles that mirror national readiness (or reluctance) to support Ukraine. By capturing attention at local, meso, and global scales, our approach offers a versatile tool for comparing relative bias across languages, user subgroups, platforms, or even historical print corpora. Ultimately, our cartographic approach reveals a troubling asymmetry: while publicly accessible data allows for an approximation of global attention patterns, the complete and unfiltered view remains largely hidden behind the closed, proprietary algorithms of major social media platforms, granting a far more comprehensive access to understanding global information flows.