  Russian propaganda outlet RT (formerly, Russia Today) produces content in
seven languages. There is ample evidence that RT's communication techniques
differ for different language audiences. In this article, we offer the first
comprehensive analysis of RT's multi-lingual article collection, analyzing all
2.4 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-temporal
embeddings. Between languages, we propose a new method to align multiple,
sparsely connected networks in an intermediate representation before projecting
them into the final embedding space. With respect to RT's communication
strategy, our findings hint at a lack of a coherent strategy in RT's targeting
of audiences in different languages, evident through differences in tag usage,
clustering patterns, and uneven shifts in the prioritization of themes within
language versions. Although identified clusters of tags align with the key
themes in Russian propaganda, such as Ukraine, foreign affairs, Western
countries, and the Middle East, we have observed significant differences in the
attention given to specific issues across languages that are rather reactive to
the information environment than representing a cohesive approach.
