Narratives of War: Ukrainian Memetic Warfare on Twitter

TL;DR

The paper analyzes Ukrainian memetic warfare on Twitter during the 2022 invasion by focusing on three prominent accounts (@Ukraine, @DefenceU, @uamemesforces) and applying a narrative framework (Hero, Victim, Villain, Fool) to explain meme virality. Using a literature-informed annotation scheme and a regression model, it shows that narratives—especially victim content—significantly boost retweet reach (up to $109\%$), while villain narratives align with countries providing more military aid, indicating a link between online narratives and offline support. The study also demonstrates strong geographical resonance, with retweet activity correlating with aid levels (e.g., $\rho=0.787$) and differential narrative resonance across regions, suggesting that memetic content can reflect and potentially influence geopolitical dynamics. These findings highlight the importance of narrative design in state-backed/propaganda-oriented memetic campaigns and prompt considerations for digital media literacy, platform algorithmic amplification, and ethical analysis in wartime information environments.

Abstract

The 2022 Russian invasion of Ukraine has seen an intensification in the use of social media by governmental actors in cyber warfare. Wartime communication via memes has been a successful strategy used not only by independent accounts such as @uamemesforces, but also-for the first time in a full-scale interstate war-by official Ukrainian government accounts such as @Ukraine and @DefenceU. We study this prominent example of memetic warfare through the lens of its narratives, and find them to be a key component of success: tweets with a 'victim' narrative garner twice as many retweets. However, malevolent narratives focusing on the enemy resonate more than those about heroism or victims with countries providing more assistance to Ukraine. Our findings present a nuanced examination of Ukraine's influence operations and of the worldwide response to it, thus contributing new insights into the evolution of socio-technical systems in times of war.

Figures (14)

• Figure 1: Distributions of narratives, actors, and emotional appeals in the messages of the three Ukrainian accounts. The accounts show marked differences in their communication strategies. @Ukraine uses a balanced mix, with an emphasis on civilians, but rarely makes fun of their target. Conversely, @uamemesforces favors the fool narrative and uses humor generously. Finally, @DefenceU favors the hero narrative, mostly evoking a sense of pride for Ukraine's military.
• Figure 2: Distributions of co-mentions of specific actors along with a given narrative for the three Ukrainian accounts. The block structure of the 2d distribution shows that malevolent narratives are used when talking about Russian actors, while benevolent ones when talking about Ukrainian actors. @Ukraine puts an emphasis on the victim narrative and @DefenceU has just two clear narratives that emerge (Ukrainian military as heroes and civilians as victims). @uamemesforces instead focuses most of its messaging on making fun of and Russian military and Putin.
• Figure 3: Distribution of co-mentions of specific actors along with a given emotional appeal. While clear preferences for distinct rhetorical styles are present across the accounts, they do not show consistent associations with specific actors.
• Figure 4: Examples of 'hero' narrative.
• Figure 5: Examples of 'victim' narrative.