Propaganda Detection in R ussian Telegram Posts in the Scope of the R ussian Invasion of U kraine (2023)

Abstract: The emergence of social media has made it more difficult to recognize and analyze misinformation efforts. Popular messaging software Telegram has developed into a medium for disseminating political messages and misinformation, particularly in light of the conflict in Ukraine. In this paper, we introduce a sizable corpus of Telegram posts containing pro-Russian propaganda and benign political texts. We evaluate the corpus by applying natural language processing (NLP) techniques to the task of text classification in this corpus. Our findings indicate that, with an overall accuracy of over 96% for confirmed sources as propagandists and oppositions and 92% for unconfirmed sources, our method can successfully identify and categorize pro- Russian propaganda posts. We highlight the consequences of our research for comprehending political communications and propaganda on social media.

## introduction because of social networks' rising use in daily life, we increasingly rely on other people's opinions when making both big and minor decisions, such as whether to vote for a new government or buy new products online. it is not surprising that by spreading propaganda, social media became a weapon of choice for manipulating public opinion. social media is rife with fake content and propaganda, which needs to be identified and blocked or removed. recent years have seen a major increase in the issue of information authenticity on social media, leading to significant research community efforts to address fake news @xcite , clickbait @xcite , fake reviews @xcite , rumors @xcite , and other types of misinformation. in this paper, we deal with russian state-sponsored propaganda disseminated in telegram. telegram is one of the most widely used venues for information sharing in russia, especially after blocking meta platforms. therefore, telegram draws much attention from organized groups that spread similar views through its channels and (most probably) funded by either state or related organizational sources. to influence the public to favor the war, the russian government implemented new regulations that gave it control over traditional media channels @xcite . the fundamentals of propaganda communication: persuasion using symbols, emotions, stereotypes, and pre-existing frameworks with the intention of swaying perceptions and influencing cognition and behavior in order to further the propagandist's agenda @xcite . our work focuses on specific pro-russian propaganda during the conflict between russia and ukraine. several researchers have documented russian propaganda during previous conflicts @xcite . this paper has two contributions: (1) it introduces a new dataset of posts about the russia-ukraine war in russian, collected from telegram channels and annotated with binary propagandarelated labels; (2) it reports the results of our case study on this dataset, where we examine a supervised method for propaganda detection. ## related work propaganda is the spread of information to influence public opinion or behavior, and it is a growing concern in today's digital era. with the vast amount of digital media available, it can be challenging to differentiate between genuine information and propaganda. in recent years, there has been a growing interest in using machine learning techniques for propaganda detection. numerous studies have attempted to classify texts' propagandistic content @xcite . for instance, @xcite allows analyzing texts at a finer level by identifying all passages that contain propaganda tactics and their types. a corpus of news articles was created and manually annotated at the fragment level with eighteen propaganda techniques. authors @xcite used the fragment level classification (flc) task dataset consisting of news articles from various sources, each annotated with labels representing one out of 18 predefined techniques. the goal of the task introduced in @xcite was to predict the propaganda techniques associated with each text fragment in the articles. the authors fine-tuned a bert model on the flc task dataset using a multitask learning approach, where the model is simultaneously trained to perform both fragment-level and articlelevel classification. another paper, @xcite , proposed a supervised learning approach using support vector machine (svm) to classify news articles as propaganda or non-propaganda. despite demonstrating fairly good accuracy, the aforementioned studies are mostly limited to english. the recent political developments have increased the number of russian-language studies. topic modeling is one of the methods that have been successfully applied in the field of nlp. in this article, yakunin et al. (2020) suggests a method for identifying texts that contain propaganda by leveraging a text corpus's topic model. with the suggested method, analysis is attempted at a much higher level of abstraction (themes and the relationships between texts and subjects rather than individual words in a phrase). other researchers in @xcite analyzed agenda creation, framing, and priming-three tactics that underlie information manipulation using both established and newly developed nlp models on voynaslov (38m+ posts from twitter and vkontakte in russian), revealing variance across media outlet control, social media platform and time. a structured topic model (stm) and a contextualized neural topic model were both used. another researcher used news stories and telegram news channels in ukrainian, russian, romanian, french, and english to examine how the media influenced and reflected public opinion during the first month of the war between ukraine and russia @xcite . the existing literature on propaganda detection offers a wide variety of methods, datasets, and perspectives that can be used to develop effective and responsible propaganda detection systems. to the best of our knowledge, our dataset is a large dataset of political posts with substantial differences between pro-war and anti-war telegram posts about the russia-ukraine war. ## conclusions and future work we are optimistic that our work will help people recognize texts that may not be objective and focus only on producing emotional feelings rather than a rational response. however, our models are trained on political texts that address the conflict in ukraine and, therefore, cannot recognize propaganda in other domains. in addition, improving the model's ability to handle scenarios such as propaganda statements in stylistically complex texts is essential to develop a more widely trainable model. we continue improving our model and will soon add a "neutral" class for correct classification. besides expanding our training set, a universal solution might be developed if we find a "typical" writing style or dissemination of propaganda across different domains. the council of the eu. 2014. eu restrictive measures against russia over ukraine (since 2014). https://www.consilium. europa.eu/en/policies/sanctions/ restrictive-measures-against-russia-over-ukraine . [online; accessed 14 may 2023]. the council of the eu. 2023. council implementing regulation (eu) 2023/571 of 13 march 2023 implementing regulation (eu) no 269/2014 concerning restrictive measures in respect of actions undermining or threatening the territorial integrity, sovereignty and independence of ukraine.