Temporally Stable Multilayer Network Embeddings: A Longitudinal Study of Russian Propaganda
Official URL
Publisher DOI
Abstract
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.
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.
Date Issued
2023-07-17
Publication Type
Working Paper
Subjects
Russia Today
•
social networks
•
multilayer networks
•
BERT embeddings
•
longitudinal content analysis
Language(s)
en
Author(s)
Matter, Daniel | |
Kuznetsova, Elizaveta | |
Vitulano, Ilaria | |
Pfeffer, Juergen |
Additional Credits
Publisher
Cornell University
Access(Rights)
open.access