CONTENT AND COLLOCATION ANALYSES OF THE RT ANALYTICAL REPORTS ABOUT UKRAINE

Authors

  • Nataliia Karpchuk Lesya Ukrainka Volyn National University
  • Bohdan Yuskiv Rivne State University of Humanities

DOI:

https://doi.org/10.29038/2524-2679-2022-03-73-86

Keywords:

propaganda, media space, content analysis, collocation analysis, RT (Russia Today)

Abstract

The Russian Federation is waging a powerful information and psychologi- cal warfare against Ukraine, the consequences of which, unfortunately, are already obvious. The Kremlin's propaganda forms the framework for the perception of objective reality and places favorable accents for Russia on Ukraine, its statehood, leaders, and the population as a whole. As a result, a distorted reality is created in the mind of the recipient, which calls into question the veracity of any statements. Understanding the mechanisms of destructive information and psychological influence is extremely important for the object of aggression, which must not only effectively resist, but also rethink, restructure its own security policy. The subject of the study is rep- resented by analytical materials of the multilingual information channel RT (Russia Today) with the hashtag «#Ukraine» for the period of September 2018 – April 2020 (990 articles). The whole period is divided into 4 subperi- ods: 1) September – December 2018 and 2) January–April 2019 – the time of 1) Poroshenko’s Presidency, 3) May–December 2019 and 4) January–April 2020 – the time of V. Zelensky’s Presidency. Content analysis made it pos- sible to find out the most commonly used words of Kremlin propaganda of each period, while the authors substantiated the reasons for the frequency of words, analyzing the events in Ukraine and Russia's reaction to them. Collocation analysis enabled to create a network that clearly demonstrates the thematic distribution of RT analytical materials. In particular, the first subnet is a purely propaganda pattern, the purpose of which is information and legal support for the actions of the Russian Federation; the second sub- net is related to specific subjects / objects and events; the third subnet is the «mononet», which presents a single topic – the reaction of Kremlin propa- ganda to the international recognition of the autocephaly of the Orthodox Church of Ukraine and the events around it.

References

Rezhym Putina: perezavantazhennya-2018, M. M. Rozumnyy (zah. red.), Kyiv: NISD, 2018, 480 р. URL: https://niss.gov.ua/sites/default/files/2019-02/Rezhym_Putina_ do_druku_new-c9ed2.pdf

Baker, P., Gabrielatos, C. and McEnery, T. (2013). Discourse Analysis and Media At- titudes: The Representation of Islam in the British Press. Cambridge: Cambridge University Press.

Berry, C., Harbord, J. and Moore, R., eds. (2013). Public Space, Media Space, Pal- grave Macmillan UK, pp. 4–12.

Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation, Journal of machine Learning research, 3 (Jan), pp. 993–1022.

Crain, S. P., Zhou, K., Yang, S. H., & Zha, H. (2012). Dimensionality reduction and topic modeling: From latent semantic indexing to latent Dirichlet allocation and beyond, Mining text data. Springer, Boston, pp.129–161.

Firth, J. R. (1957). Papers in Linguistics 1934–1951. Oxford: Oxford University Press.

Ghorab, M. R., Zhou, D., O’Connor, A., & Wade, V. (2013). Personalised infor- mation retrieval: Survey and classification, User Modeling and User-Adapted Interaction, 23 (4), pp. 381–443.

Habermas, J. (1989). The Structural Transformation of the Public Sphere, Cambridge: MIT Press.

Silge, J., Robinson, D. (2017). Text Mining with R: A Tidy Approach (1st. ed.). O'Reilly Media, Inc. URL: https://www.tidytextmining.com/

Soumya, K., Shibily, J. (2014). Text Classification by Augmenting Bag of Words (BOW) Representation with Co-occurrence Feature, IOSR Journal of Computer Engineering, 16, pp. 34–38.

Yuskiv, B., Karpchuk, N. (2021). Dominating Concepts of Russian Federation Pro- paganda Against Ukraine (Content and Collocation Analyses of Russia Today), Politologija,

№ 102, Issue 2, р. 116–152. URL: https://www.journals.vu.lt/politologija/article/view/24506

Zhang, Y., Jin, R., Zhou, Z. H. (2010). Understanding bag-of-words model: a sta- tistical framework, Int. J. Mach. Learn. & Cyber., 1, 43–52. URL: https://doi.org/10.1007/ s13042-010-0001-0

Published

2022-10-21