Russian Influence Operations during the Invasion of Ukraine


  • Joseph Littell US Army
  • Nicolas Starck



Influence Operations, Russia, Ukraine, Machine Learning, Natural Language Processing, Topic Modeling, Disinformation, misinformation


Prior to their invasion of Ukraine, the Russian Federation was seen as having a vastly superior ability to conduct operations in the information environment, particularly their ability to influence foreign audiences, when compared to their western counterparts (Cunningham, 2020). However, the efforts of Ukraine, NATO allies, and other aligned nations to conduct operations in the information environment with intelligence pre-bunking, traditional diplomacy, and sanctions, blunted many of Russia’s best efforts. Further tactical and operational failures from the Armed Forces of the Russian Federation also undercut the salience of Russian messaging campaigns. This does not mean that Russia’s efforts in the informational environment fully failed or did not adapt to these actions in the lead-up and continuation of the war. Through the use of the Natural Language Processing (NLP) technique transformer-based topic modeling (Grootendorst, 2022) and the causal inference technique Bayesian Structural Time Series analysis (Brodersen, 2015) this paper looks to both qualitatively and quantitatively examine how Russian state media on Twitter reacted, changed narratives, and focused efforts regionally from January 1st through September 1st, 2022. Through this analysis we argue that Russian efforts in Europe may have been of limited success. We further argue that by shifting focus, Russia gained influence in South America, and Middle East and North Africa, where their influence operations faced minimal obstructions, such as sanctions, and a latent anti-western sentiment.