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Russian propaganda outlet RT (formerly, Russia Today) produces content inseven languages. There is ample evidence that RT's communication techniquesdiffer for different language audiences. In this article, we offer the firstcomprehensive analysis of RT's multi-lingual article collection, analyzing all2.4 million articles available on the online platform from 2006 until 06/2023.Annual semantic networks are created from the co-occurrence of the articles'tags. Within one language, we use AlignedUMAP to get stable inter-temporalembeddings. Between languages, we propose a new method to align multiple,sparsely connected networks in an intermediate representation before projectingthem into the final embedding space. With respect to RT's communicationstrategy, our findings hint at a lack of a coherent strategy in RT's targetingof audiences in different languages, evident through differences in tag usage,clustering patterns, and uneven shifts in the prioritization of themes withinlanguage versions. Although identified clusters of tags align with the keythemes in Russian propaganda, such as Ukraine, foreign affairs, Westerncountries, and the Middle East, we have observed significant differences in theattention given to specific issues across languages that are rather reactive tothe information environment than representing a cohesive approach.

Temporally Stable Multilayer Network Embeddings: A Longitudinal Study of Russian Propaganda