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

2026-03-18

AI Summary

This study addresses a gap in existing research by providing the first longitudinal, cross-lingual comparison of global attention to Ukraine on social media during the 2014 and 2022 Russia–Ukraine conflicts. Drawing inspiration from DNA microarray analysis, the authors propose a macroscopic visualization framework that employs log-fold overexpression and baseline frequency normalization to examine attention dynamics toward “Ukraine” across 28 languages on Twitter from 2008 to 2023. The approach enables systematic comparison of attention patterns across languages and time periods, revealing two nearly non-overlapping language clusters with distinct onset, peak, and decay characteristics during the two crises. The findings elucidate temporal evolution patterns in global public attention while also highlighting potential limitations imposed by platform algorithms on data completeness.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Application Domains: Humanities & Computational Social Science
• Computer Vision: Language and Vision

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Social Networks and Social Media: Social media analysis through the lenses of networks
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

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 saudience. 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.

Problem

• Ukraine
• Twitter
• attention dynamics
• cross-lingual analysis
• crisis events

Innovation

• log over-expression
• cross-lingual attention mapping
• longitudinal social media analysis
• DNA microarray-inspired cartography
• attention asymmetry