The Evolution of Pro-Kremlin Propaganda From a Machine Learning and Linguistics Perspective (2023)

Abstract: In the Russo-Ukrainian war, propaganda is produced by Russian state-run news outlets for both international and domestic audiences. Its content and form evolve and change with time as the war continues. This constitutes a challenge to content moderation tools based on machine learning when the data used for training and the current news start to differ significantly. In this follow-up study, we evaluate our previous BERT and SVM models that classify Pro-Kremlin propaganda from a Pro-Western stance, trained on the data from news articles and telegram posts at the start of 2022, on the new 2023 subset. We examine both classifiers’ errors and perform a comparative analysis of these subsets to investigate which changes in narratives provoke drops in performance.

## introduction and related work fake news has been shown to evolve over time @xcite . a piece of news is often modified as it spreads online by malicious users who twist the original information @xcite , while an imperfect replication process by other users leads to further distortion @xcite . @xcite showed that the disinformation techniques, parts of speech, and keywords stayed consistent during the evolution process, while the text similarity and sentiment changed. moreover, according to their scoring, the distance between the fake and evolved fake news was more prominent than between the truth and the initial fake news. the evolved ones sound more objective and cheerful and are more difficult to detect. @xcite also observed significant differences between real and fake news regarding evolution patterns. they found that fake news tweets underwent a more significant number of modifications over the spreading process. inn case of fake news and disinformation originating in state-run outlets, we talk about propaganda. in this and previous studies, we focus on russian propaganda. @xcite @xcite . it has been shown that the russian presidential administration exercises coordinated control over media advertising budgets and editorial content whilst maintaining an illusion of media freedom by letting a small number of minor independent media outlets operate @xcite . hence, the adaptations to kremlin's political agenda are an additional factor that contributes to how russian fake news evolves. modern kremlin propaganda fundamentally appeals to former greatness, glorification of the russian empire, the victory in world war ii, the soviet union's past and the narrative of 'facing the west' @xcite . looking at the key narratives between the beginning of 2022, and the start of 2023, after a year of unsuccessful assault we observe several shifts in the narrative. at the beginning of the war, the official goals and objectives were identified by obscure terms such as "denazification" and "demilitarization" of ukraine. at the same time, a fight against the neo-nazis has become an established rhetoric of the highest officials. "american biolabs in ukraine", "8 years of genocide in donbas" and the claim that the ukrainian government is responsible for shelling its own cities @xcite became the most frequent topics. after almost one year, russian officials now openly recognize shelling of civilian electric infrastructure @xcite @xcite , while propaganda directed to the external audience becomes majorly blackmail threatening western countries to prevent them from supplying ukraine (faulconbridge, 2022a). as for the internal audience, the main objective is to support mobilisation efforts in russia @xcite . in our initial study @xcite , we proposed two multilingual automated pro-kremlin propaganda identification methods, based on the multilingual bert model @xcite and support vector machine trained with linguistic features and manipulative terms glossary. considering the aforementioned transformations, we hypothesised that our models' performance should drop on the 2023 data. in this follow-up study, we measured how the models trained a year ago perform on current news from the same sources. we also analysed how their language changed according to our linguistic feature set. in section 2, describe the experimental setup and the new data set. we present our results in comparison to those from 2022 in section 3. in section 4 we carried out an error analysis of the svm and bert models. for the svm we contrasted the linguistic feature distributions in the groups of errors. for the bert model, we applied a simplified word importance approach to gain insight into vocabulary and morpho-syntactical categories. in section 5, we compare the 2022 and the 2023 data sets to see how propaganda evolved overall in our given context. finally, we discuss our key findings and draw a conclusion in section 6. ## results the full test in 2022 corresponds to the performance on 8700 samples of the original test set, while the small is a random sampling of the original 2022 test set to correspond to the size of the 2023 set and makes them comparable. although we also took an average of 5 seeds, the perfect comparison is complicated since we cannot ensure a balanced representation of the test samples from 2022 and 2023 in their complexity. as shown in if the new next word changed the prediction value and its probability, it was recovered into either the list of words inducing pro-kremlin or pro-western prediction, separately for 2022 and 2023. we analysed extracted lists with linguistic features extraction script to see if there are some similarities in how experts and bert choose propaganda features. thus, the first finding is that bert identifies the names of the sources appearing in the text and connects them to the prediction classes. for instance, 'ziua', the name of a romanian tabloid is one of the most frequent words we extracted for romanian words, which changes prediction into 'propaganda'. in contrast 'activenews', a neutral romanian news outlet always changed prediction value into 'pro-western stance'. even more, in 2022 french data a link to russian 'ria' news also was accurately determinant for propaganda class. in 2023, the main word indicating propaganda in russian news was 'main/head', for the french 'authority' and for the romanian 'treaty'. in contrast, the main words for pro-western prediction for the russian were 'announce' and 'sovereign default'. in 2023, the main words indicating propaganda for romanian were 'sanctions', 'tribunal' and 'war'. in 2022, the word 'war' was actually a determinant for propaganda, while words describing punishment were not typical topics for romanian media, they were, however, already present in ukrainian one. it is possible that keywords bert learnt in one language are projected to others in the multilingual model. in 2023 pro-kremlin propaganda in ukrainian news would focus on the word 'putin' while predicting for pro-western news are words 'ukraine' and 'ukrainians'. in ukrainian pro-western news, words connected to national institutions such as 'government', 'minister', and 'state' are significant. in the russian language, a keyword most reliable for prediction of the liberal side is 'orcs', the way how ukrainians call russian soldiers (while russia is called 'mordor' by the analogy of tolkien's lord of the rings). by classifying the resulting words according to categories of linguistic features, we can see that many categories are matched. the most popular parts of speech are adjectives, abstract and proper nouns, and high-modality words. many of them express either strongly negative or positive connotations. similar to our initial study results, reporting words are highly predictive of the pro-kremlin stance in the russian language in 2022. syntactical features such as different types of clauses are present to a lesser degree. hence, morphological information may be used more than syntactical one for predictions. some glossary keywords were also used by bert's model, e.g., 'war', 'special operation', 'dnr', 'lnr', 'negotiations', and 'kremlin'. ## comparative analyses we decided to look into the evolution of propaganda, by comparing the averages for each feature between 2022 and 2023 for each subset. we used z-score normalized averages. we could not use medians, which are a better choice, because the data is sparse, most of the medians equal 0, which complicates normalization and significance testing. we chose the mann-whitney u-test, as the events are not paired and are not normally distributed. see the comparison in figure 1 . the most substantial difference is seen for the keyword "kiev regime", which became a lot more frequent in the russian telegram, where users also started discussing more negotiations and 'the west', making more claims, and using more assertive words, adverbs and other high-modality words. russian state-run outlets on the other hand started using considerably less 'special military operation' wording but also dropped the rhetoric of 'the republic of crimea', 'lnr' and 'dnr', which the russian federation annexed and considers its own regions, rather than independent republics. it also speaks less of negotiations, sanctions, genocide, fake news and belorussia. russian liberal news did not change its style and narrative, nor did english-speaking, french pro-western and french pro-kremlin news. romanian pro-kremlin data became less emotional. we can observe a drop in most negative and positive emotions, especially in 'trust'. there can be seen more abstract nouns and conditional clauses, which are more typical for the pro-western narrative but also relative clauses and claims, which can usually be seen more in pro-kremlin news. on the other hand, pro-western romanian media has much more negative sentiment than at the beginning of 2022, there is more anger and fear. they talk more about the deceased and the attacks, calling out kremlin more directly. ukrainian pro-western news became more neutral, as negative and positive emotions calmed down, particularly trust. there is less mention of genocide, embargo, negotiations and sanctions, which were more important topics for 2022. a rise in the clause of time, adverbs and especially proper nouns is significant, reflecting mostly the discussion around armament supplies. in ukrainian telegram, on the contrary, there is more anger, awaiting, and sadness. the high effect size for the keyword 'fake' reflects ukrainian efforts to debunk kremlin propaganda. stylistically, the language possesses more adjectives, and subordinate clauses of reason, purpose and condition. the potentially pro-kremlin news in ukrainian, which seems to have partly changed their allegiance, shows more emotion of trust and fear, it is in general more expressive, with a higher number of adverbs. it uses the russian manipulative 'belorussia' term and 'belarus' but leans more towards the latter. for comparing the languages see appendix a.1. ## discussion and conclusion we applied an svm with linguistic features and bert multilingual model trained on the data from the beginning of 2022 to the new data from 2023. since it is complicated to balance the complexity of the test sets, the true accuracy of the model lies anywhere between the full and the small 2022 test results, depending on how explicit the propaganda is. however, it is still possible to claim that both models successfully accurately identify a pro-western stance. both classifiers are more prone to false positives. as we showcased in the svm model's error analysis, some distributions of significantly important features from our previous study, like abstract nouns and adjectives, are now similarly distributed between false positives and true positives. at the same time, the bert model is prone to attributing the class according to the news source name mentioned, which can lead to the model predicting everything describing or even debunking these outlets as propaganda. overall, we observed that morphological information may be used more than syntactical one for predictions in bert, while according to our initial study, a tendency towards some subordinate types distinguishes well the two stances. at the same time, the rise in temporal clauses in pro-western stance, which in 2022 was highly significant for pro-kremlin news may explain the higher miss-classification rate of the svm. the word 'war' appeared highly predictive for both svm and bert. indeed, at the beginning of the