Interpretable Models for Detecting Linguistic Variation in R ussian Media: Towards Unveiling Propagandistic Strategies during the Russo- U krainian War (2025)

Abstract: With the start of the full-scale Russian invasion of Ukraine in February 2022, the spread of pro-Kremlin propaganda increased to justify the war, both in the official state media and social media. This position paper explores the theoretical background of propaganda detection in the given context and proposes a thorough methodology to investigate how language has been strategically manipulated to align with ideological goals and adapt to the changing narrative surrounding the invasion. Using the WarMM-2022 corpus, the study seeks to identify linguistic patterns across media types and their evolution over time. By doing so, we aim to enhance the understanding of the role of linguistic strategies in shaping propaganda narratives. The findings are intended to contribute to the broader discussion of information manipulation in politically sensitive contexts.

## introduction the russo-ukrainian war has intensified the need to understand media manipulation and its societal impacts. there has been an increased number of endeavors for propaganda detection, in general and on the russo-ukrainian war specifically. since language variation can be driven by external factors such as social, political, or cultural influences, studying linguistic change in the context of propaganda can help detect it more accurately. this argument is further supported by the fact that disinformation changes and evolves over time @xcite , as is the case with russian propaganda @xcite , which has been used by the government to justify the invasion and gain support from its population. moreover, research has shown that linguistic change can occur not only diachronically, but also across diverse contexts, such as different political viewpoints @xcite . by comparing traditional mass media, i.e., press and tv, with social media in russia, @xcite found that propaganda frames foot_0 differ between these two text types: state media are targeted at more passive audiences, whereas social media seek to convince those searching for alternative sources of information. this suggests that there might be fewer regime supporters on social media, and thus the political stance of the users might differ between the two text types. this paper presents a research framework to analyze russian state-controlled media and social media, which will allow us to answer the following research questions: (1) how language in these two text types linguistically differs and might reflect propaganda strategies (e.g., the use of euphemisms); (2) how it might have changed over time. as a result, we expect to see linguistic variations between the two media types, since they use distinct propaganda frames and strategies. specifically, we might find a tendency towards euphemistic choices to prevail in state-controlled media texts in comparison to social media posts, such as by replacing war with special military operation (the former term is less likely to be propaganda, cf. @xcite . additionally, by conducting our analysis, we anticipate to trace the diachronic evolution of russian propaganda about the war in ukraine. even though propaganda and disinformation detection is a common natural language processing (nlp) task, few studies have focused on linguistic change as a possible indicator of information manipulation. furthermore, recent research relies on transformer-based architectures exploiting contextual embeddings for propaganda detection and classification into techniques (e.g., @xcite . while these methods perform relatively well, they heavily rely on annotated data and explainability remains a major issue, as they do not allow fully capturing on which basis the classification of propagandistic texts is motivated (cf. @xcite . in this study, besides using word embeddings, we also propose interpretable methods applied to the analysis of language variation and change. word embeddings @xcite will allow us to model semantic shifts. kullback-leibler divergence (kld; @xcite is employed to detect and analyze features contributing to change across linguistic levels @xcite @xcite @xcite . to capture more nuanced changes in the local linguistic context, we use surprisal, which models the (un)expectedness of words in particular contexts @xcite . the combination of these methods would help us detect distinctive features of linguistic change, providing highly interpretable results. their detailed description is provided in section 4. ## related work since the beginning of russia's full-scale invasion of ukraine, there have been a number of attempts to combat russian propaganda with the help of nlp techniques. some studies applied both traditional and neural machine learning to detect pro-kremlin propaganda with promising results @xcite @xcite . arguing that dehumanization leads to extreme violence, @xcite trained a few binary classifiers to detect dehumanizing language of ukrainians on russian social media, with the spert model outperforming the rest. other studies delved into the computational analysis of russian propaganda about the war. for instance, @xcite created the wartime media monitor (warmm-2022) corpus, which includes publications on the russo-ukrainian war and consists of two parts: state media and social media, and used it to analyze major propaganda themes and strategies. in their later study, @xcite , by working on the same dataset, explored the differences between propaganda frames representing diverse semantic entities in the two subcorpora. they identified the following frames: dependence (the narrative about ukraine's depen-dence on the west), dehumanization (using dehumanizing language towards ukrainians), normalization (downplaying the effects of the war on the everyday life in russia), and disinformation (presenting news from ukraine and the west as fake). the researchers found that the press and tv applied the dehumanization frame (which is in line with the results reported by @xcite , as well as dependence and normalization, while social media used the disinformation frame. these strategies, on the one hand, aimed to pacify the regime supporters who mostly consumed traditional media, and on the other hand, tried to mobilize the users of social media by employing the disinformation frame. the findings by @xcite also confirmed that the mobilization strategy was used by the government to target the russian population. this indicates that the two text types are distinct from each other, as they are aimed at different audiences (at the semantic as well as other linguistic levels). similarly, park et al. ( 2022 ) analyzed the media effects of russian news about the war; however, instead of comparing press and tv with social media, they looked into state-affiliated and independent outlets on two online platforms, vkontakte and twitter. they found that since the start of the fullscale invasion, independent media outlets have predominantly used the term war, while state-affiliated outlets have frequently opted for the euphemism (special military) operation. the same difference was observed by @xcite between pro-russian and pro-ukrainian telegram channels, indicating that political opinions might influence semantic choices and phrasing. using the term special military operation was also given as an example of the normalization propaganda frame by @xcite . this is in line with @xcite results, who trained two classifiers based on svm and bert to detect pro-kremlin propaganda, and found that the word war was highly predictive for both models, meaning that a text containing it was more likely to be labeled as not propagandistic. the authors explain it by the fact that this term was deliberately avoided by government officials and even became illegal in russia. consequently, it rarely appeared in pro-kremlin news, which relied on euphemisms instead. apart from linguistic variation between the stateaffiliated and independent media, @xcite also observed differences in the two platforms (vkontakte vs. twitter, particularly divergent framing strategies), as well as across time (before vs. after the beginning of the full-scale invasion, e.g., an increase of frequency of terms related to war). this confirms @xcite hypothesis that semantic change can occur both diachronically and in distinct contexts, such as divergent political viewpoints. since traditional media and social media may reflect differences in the stance of the users, we presume that language might also vary between these two text types. diachronic variation of war narratives was also analyzed by @xcite , who looked at the evolution of pro-kremlin propaganda within the first year of the full-scale invasion. compared to the beginning of 2022, they found an increase in the use of the term kyiv regime, claims, assertive words, adverbs, and other high-modality words, as well as the mention of the west and negotiations on russian telegram at the start of 2023. in contrast, special military operation, negotiations, sanctions, genocide, fake news, and belarus were discussed less frequently in the russian state-run media in 2023 in comparison with 2022. in a similar vein, burovova and romanyshyn (2024) observed varying temporal dynamics of the dehumanization rhetoric, whose changes coincided with important events before or after the start of the full-scale invasion. in particular, they found that certain types of dehumanization began to rise shortly before the invasion and declined at its onset, suggesting that dehumanization plays a preparatory role in legitimizing acts of genocide. these developments reveal important shifts in propagandistic narratives over time. ## data for our pilot study, we are using the warmm-2022 corpus @xcite , which is a collection of 1.7m posts on the russo-ukrainian war. our motivation for choosing it is two-fold. firstly, the corpus includes two text types targeting different audiences. the state-controlled mass media include 24.4m tokens of press and 1.7m tokens of tv transcripts, and their style is more formal. social media posts consist of 268.4m tokens and are characterized by limited governmental control and less formal register. whereas the former text type promotes state-imposed propaganda, the latter includes both publications by regime supporters and anti-government voices. these differences be-tween the text types would allow us to study linguistic variation in divergent contexts. secondly, the warmm-2022 corpus is diachronic: the state media subcorpus covers the period from february until september 2022, whereas the posts on social media span from july to september 2022, which is useful for analyzing linguistic change over time. ## preliminary and expected results drawing from @xcite work, we anticipate finding differences and/or similarities between state and social media, as well as tracing the evolution of russian propaganda over time by applying the above-mentioned methods. this would allow us to study linguistic change both diachronically and across media types. we might also gain insights into the interplay between the text types. specifically, narratives that originate in the official media might influence social media discourse. this could happen through the repetition and reinforcement of state-approved messages by pro-government social media users and the dissemination of mainstream propaganda by bots or paid commentators @xcite . as the first step of our pilot study, we conducted some experiments by applying kld to a small subset of the warmm-2022 corpus, and we could already see some of the results we expected. specifically, we compared the usage of nouns in social and state media posts from july 30 and 31, 2022 (approx. 2 million nouns). while the direct term война foot_1 is the most distinctive noun for social media, state media mostly uses opaque euphemisms like спецоперация foot_2 , ситуация foot_3 and демилитаризация foot_4 . this is in line with previous studies, which showed a clear distinction between the words denoting the war used in propagandistic or non-propagandistic texts @xcite , pro-russian or pro-ukrainian news @xcite and state-affiliated or independent outlets @xcite . another interesting observation is that there is a high contribution of words such as правда foot_5 and факт foot_6 to the language of social media, as opposed to that of press and tv. this could indicate the government's efforts to employ the disinformation frame, which, as was shown by @xcite , is predominant on social media as a means to discourage users from seeking out other sources of news that contradict the pro-kremlin narratives. in the future, we plan to do a more comprehensive kld analysis comparing state and social media posts from the whole warmm-2022 corpus, as well as studying diachronic linguistic change in the context of propaganda and applying other methods mentioned in section 4, namely surprisal and word embeddings. as a more ambitious goal, we hope that our work will contribute towards combating disinformation, specifically in war contexts. in terms of practical applications of our methodology, we expect it could be employed in studying other political or historical events. ## conclusion this work underscores the potential of open, transparent methodologies to democratize access to knowledge and foster resilience against disinformation. by leveraging interpretable methods such as kld, surprisal, and word embeddings, our study aims to provide a robust framework for detecting and analyzing propaganda strategies in russian state-controlled and social media. by systematically examining linguistic change both across text types and over time, our study contributes to a deeper understanding of propaganda mechanisms and their societal implications. it also highlights the importance of combining interpretability and reproducibility in computational linguistics research, particularly in political contexts. in addition to its academic contributions, this research has significant practical implications. it equips researchers, policymakers, and media analysts with tools to critically examine information landscapes and identify deliberate attempts to influence public opinion. ultimately, by demonstrating how linguistic change can be an indicator of propagandistic strategies, we aim to advance efforts to counteract disinformation and enhance media literacy.