This article reports a corpus-based comparative analysis of the English-language transcripts of President Volodymyr Zelenskyy's nightly video addresses during two one-month periods of the Russia-Ukraine war: 24 February-23 March 2022 and 24 February-23 March 2023. Two specialized corpora were compiled from the official presidential website (≈60,823 and ≈28,300 tokens, respectively) and processed in LancsBox. The study combines quantitative procedures-word frequency profiling, keyword analysis using simple maths and Cohen's d, concordance inspection, and collocation analysis (GraphColl)-with qualitative political discourse analysis to identify salient themes. Results indicate similarity in core narratives (national unity, defence, invasion) alongside marked shifts in lexical choice and stance. In 2022, keywords represent immediate defence and crisis (e.g., invaders, invasion, corridors, Mariupol, Melitopol), while in 2023 the discourse moves toward actors and coordination (e.g., Bakhmut, warriors, occupier/brigade, cooperation, justice, security). Collocational profiling of the node war shows 2022 patterns centred on against, after, Ukraine, day, world, whereas 2023 emphasises Russia, full-scale, during, suggesting a stronger attribution of agency and an increasingly international framing. Concordance choice further shows a move from early shock/negation toward more institutional, coalition-oriented messaging. Across both corpora, personalization via the lemma Putin is notably limited, which supports a strategic focus on broader geopolitical framing. The findings show both similarities and differences in President's crisis communication across the first and thirteenth months of the war.
emotional appeals to captivate and engage the audience. They are amplified through media channels, reaching wider audiences through live broadcasts, online streaming, or social media platforms. These platforms allow for greater dissemination and engagement with the address, enabling individuals to share their reactions and discuss the content in real-time.
In light of the full-scale war between Russia and Ukraine, there is a research interest to explore how the addresses are communicated by the President of Ukraine in his daily appeals to the nation, particularly through linguistic means. Specifically, it focuses on the linguistic means of representation of the war in the president’s addresses during two periods: 1) 24.02.2022-23.03.2022, and 2) 24.02.2023-23.03.2023 marking the first months of the first and second year of the war conflict. The study utilizes corpus linguistics methods to analyze two specialized corpora compiled by the authors for the outlined objectives. The key research questions were formulated as follows:
RQ1: What are the key lexical features characterizing the addresses delivered by the President in the specified time frame?
RQ2: To what extent do the principal themes revealed in the addresses of both periods align or diverge?
The paper proceeds with the contextualization of the current study in the literature connected with the subject at issue. Subsequently, it describes the data collection procedure and the analysis methodology employed. Finally, the paper concludes by presenting an analysis and interpretation of its principal findings.
Since the outburst of the war in Ukraine, several works on war discourses have been published. The
majority of them analyses the means of war coverage in different media, for instance, Natalia Petiy
investigating English-language media searches for cognitive metaphors utilized in depicting the war in
Ukraine “The results of the analysis indicate that the war in Ukraine is frequently represented as a game
or a battle between good and evil” [21]. Some authors have focused on specific verbal and nonverbal
components of political addresses delivered by Volodymyr Zelenskyy [
Other authors have driven the further development of war discourse in Ukraine and the study carried
out by Lőrincz [17] applies corpus analysis in Eastern and Western English language news releases to
identify their main topics. The paper reveals that lemmas Putin and Putin’s predominantly appear in the
subject and attribute positions among the collocates of the term war, while lemma Zelenskyy is more
commonly found in the object position. She concludes that the syntactic pattern suggests that lemma
Putin is often associated with the agent or instigator of the action [17]. A recent study by Yiğit Salihoğlu
and Çiğdem Karatepe similarly examined online news about the Ukraine-Russia war on BBC News and Al
Jazeera English and came to some surprising conclusions. They suggest that “the illustration of news and
the used language seemingly attempted to diminish the unpleasant aspects for Ukraine and its society”
[26]. A more comprehensive description can be found in an analysis of online public sentiments in tweets
related to the war. According to the study by Rahat Gulzar et al, it is revealed that most tweets are
negative, and the total number of tweets declines over time [15]. Seminal contributions have been made
by Baladrón‐Pazos et al based on Spanish political tweets and their results show that a political party's
communication generally meets the rules of political correctness and moderation [
There also have been several studies that investigate the ways how the president of Ukraine
V. Zelenskyy and the president of Russia V. Putin are approached by media sources. The study by Raza and
Malik utilises BBC as a data source to compare the media representation of Putin and Zelenskyy with the
tools of multimodal CDA. They conclude that BBC’s profiles are not neutral and “represent Putin as rigid,
vastly powerful and a threat, whereas Zelensky is shown as amiable but dauntless in the face of war and
political strife” [24]. From a different perspective, the paper by Innocent Chiluwa and Jurate Ruzaite
compares the war rhetoric of Putin and Zelensky [
To conduct this study, two specialized corpora of English language addresses delivered by President Volodymyr Zelenskyy were compiled, covering the period from February 24th, 2022, which marked the beginning of the Russian military assault, to March 23rd, 2022, and from February 24th, 2023 to March 23rd, 2023, thus covering the first months of each war year consequently. The first corpus (referred to as Corpus-22) consisted of about 60 addresses and speeches comprising a total of 60,823 words. The second corpus (referred to as Corpus-23) comprised around 37 files totalling 28,300 words. The referred video addresses, as well as transcripts of these speeches, are available in English on the official website of the President of Ukraine.
The present study covers two periods of one-month length within a year of the full-scale Russian war against Ukraine employing corpus linguistics methods. Corpus research is particularly advantageous for analyzing political discourse on war as, according to Paul Baker, it reduces researcher bias, facilitates the incremental effect of discourse and reveals resistant and changing discourses (2006, 10-15). This approach allows for a more robust and efficient study of linguistic data, enabling researchers to formulate more reliable interpretations and generalizations regarding prevalent discourses within a corpus [17]. McCarty discussing the issue of multi-word units in the scope of lexis emphasises that corpora reveal the regular, patterned preferences in given contexts, and “show how large numbers of users separated in time and space repeatedly orient towards the same language patterns when involved in comparable social activities” [19]. As a research tool, corpus analysis is useful in identifying themes and patterns across texts, and uncovering the underlying meanings prevalent in the text.
To process the target corpora, the Lancsbox software package was utilized. The objective was to
conduct a comparative examination of the linguistic features of the president’s addresses in each first
month of the war. We employ different techniques on the corpora to ensure the quality of our
analysis, as the methodological triangulation “facilitates validity checks of hypotheses, anchors
findings in more robust interpretations and explanations, and allows the researcher to respond
flexibly to unforeseen problems and aspects of the research” [
Frequency word analysis reveals the aboutness of the created corpora and as Paul Baker observes
“frequency is one of the most central concepts underpinning the analysis of corpora” and also “one of
the most oft-heard misconceptions of corpus linguistics” [
The next tool used is the keyword analysis which is employed to identify salient words in the
corpus and the aboutness of the textual data. Baker and Ellece define keyness as “the relative
frequency of a particular linguistic item in one text or corpus when compared against another text or
corpus via statistical tests of significance (usually chi-squared or log-likelihood)”[
Collocations, thought of as the “company that a word keeps”, show some regularity in the
appearance of words. Collocation studies show that a good deal of word combinations are “the
probabilistic outcomes of repeated combinations created and experienced by language users”[18].
The notion of collocation shifts the emphasis from a single word to pairs of words as integrated
chunks of meaning. According to Stubbs, collocation analysis helps to “show the associations and
connotations they have, and therefore the assumptions which they embody” [
The tools mentioned above are combined with the analysis of concordances, necessary to avoid
making presuppositions about the ways that words are used within a text. As Baker states, a
concordance analysis is one of the most effective techniques which allows researchers to carry out
this sort of close examination [
The research structure includes the following stages: first, frequency words analysis is carried out and then positive and negative keywords are identified using the Words tool of the Lancsbox software. In particular, simple maths and Cohen’s D statistics are employed to identify these keywords. Frequency cut-off points were not applied.
The top 100 keywords were examined. Function words were of no interest to the study as they primarily serve structural purposes rather than carrying substantive content. Consequently, they were disregarded in the analysis. For the analysis of collocates of the node “war”, the following criteria were applied: Statistic: 01-Freq | Span: 5-5| | Statistic value threshold: 5.0. By adopting this default statistics approach to the study, reliability was ensured by determining the quantitative and qualitative features in the language of the corpora.
Keyword statics (simple maths). To compare the most frequent words in the target corpora, the Words tool of the Lancsbox software was employed. Specifically, the keyword technique served to compare the lexical characteristics of the corpora. As a result, positive keywords, negative keywords, and lockwords were identified (Table 1). The positive keywords indicate the lexical choices that were more prevalent in Corpus-22 compared to Corpus-23. On the other hand, the negative keywords reveal the linguistic preferences of Corpus-23 when contrasted with Corpus-22. Lastly, the lockwords represent words that appeared with similar frequencies in both corpora. Because the keywords technique is a reliable method for uncovering the aboutness of the corpus, it was used to determine the principal discourses in the President’s speeches during the first and thirteenth months of the war.
The tabulated data reveal that the President’s addresses delivered in Corpus-22 primarily focus on the defence strategy of the invaded country, often referencing specific idea notions. Consequently, lemma invaders has the highest frequency, while corridors (combined with humanitarian) demonstrate the depth of the crisis within the country. Notably, lemmas Melitopol and Mariupol are also highly salient while they do not appear in Corpus-23. It is explained by the fact that these two cities were the first ones occupied and battled for during the first month of the war. Contrary, lemma Bakhmut ranks top in Corpus-23 as the situation in that area was disastrous at that period. The keyword analysis of Corpus-22 also includes lemmas posthumously, lieutenant, and colonel as part of the President’s awareness of the individual warriors who die in the war. The focus of attention is given more to the internal crisis (lemmas invasion, invaders, residents) rather than the international problem (only lemma Moskow and Israel are used in the first target corpus). While lemma invader occupies the top position in Corpus-22, it ranks fourth (but is translated as occupier) among the keywords in Corpus-23 and immediately follows lemma warriors thus showing the interrelated process of the defence strategy. The keyword analysis of Corpus-22 points to closer attention to the lemmas plane, vehicle as they are regarded as the main instruments in the ongoing events. Additionally, Corpus-23 highlights lemmas warriors, occupier, brigade, fellow, brigades, and staff. This indicates that the target corpus follows the actors of the warfare more closely than its first counterpart. In contrast, the salience of lemmas seen, look, peaceful in Corpus-22 may suggest the President’s position to solve the situation from the only perspective of settling peace in the country. The presence of lemmas thank, separate, justice, discussed, results, cooperation, formula, help in Corpus-23 also suggests that the President’s policy changes in the direction of peace realisation and he aims at its multidimensional and international support. The presence of lemmas bravery and frontline also suggests that Corpus-23 is primarily concerned with the situation on the battlefield while seeking ways of resolving the conflict through dialogue and cooperation with partners.
The results of the keyword analysis obtained using Cohen’s D test are presented in Table 2.
Keywords statistics (Cohen’s D). By applying the Cohen’s D statistics, we compared the obtained data and found that Corpus-22 (Table 2, column 1) describes the main agents of the warfare through lemmas they, us, invaders, people and civilians. Notably, lemma war occupies only fourth place in Corpus-22 while in the next one, it is not indicated at all. The attempt to explain the situation is achieved by employing the lexemes with informative meaning (e.g., lemmas because, but, what, if, so). The only lexeme denoting particular actions to be done is lemma corridors which appear the last one in our list. On the other hand, the keywords from Corpus-23 (Table 2, column 2) primarily focus on achieving the main concepts of peace (e.g., lemmas health, glory, separate, international, cooperation, security, good) as well as on the agents of the conflict (e.g., lemmas fellow, warrior, brigade vs occupier, terrorist). We may also find some common verbs which demonstrate a particular activity done to change the situation (e.g., lemmas thank, helps, held) while in Corpus-22 (Table 2, column 1) there are ones of unspecified meaning (e.g., lemmas know, have, say).
We may also find quite a lot of keywords in Corpus-22 which show the determination and clear vision of the current situation (e.g., lemmas not, but, what, if, so) as in the following examples: If we protect Kyiv, we will protect the state; or And even if you destroy all our cathedrals and churches, you will not destroy our sincere faith in God, in Ukraine. The keyword if (98 hits, 16.11 rel.freq.) is used together with the present tense verbs which means that the President is fully aware of the near future:if someone thinks, if they can accumulate, if everyone leaves, if you try to hide, if anyone still doubts, if the invasion continues. Lemma not has 584 hits (96.02 rel.freq.) in Corpus-22 while only 100 hits (35.34 rel.freq.) in Corpus-23. Some examples of Corpus-22: Even the border between them was only conditional, only on maps, but not in the soul. Not in the soul; This is not to be changed by missiles; They are not afraid of even that. The lower frequency of the negative sentences in the second portion of the corpus (February-March 2023) also shows that the whole rhetoric of the President’s speeches changes and becomes more impersonal and cooperative. We can find the lemmas and (977 hits, 345.23 rel.freq.), who (292 hits, 103.18 rel.freq.). The sentences from the corpus to illustrate the use of conjunction and are the following: The Staff considered the production and supply of ammunition and weapons; Separately and in great detail… This is a very valuable and value-based cooperation. Lemma who is found in various contexts and refers to multiple actors of the discourse: Glory to each and everyone who is in combat; Thank you to everyone who helps us; I want to address those who are still waiting.
Concordance. A concordance is a comprehensive index of the words used in a text or a body of text. Ordinarily, it will not only index but also cite all passages in which a given word occurs. To verify the reliability of the inferences derived from the quantitative data, further investigation was conducted by analysing the concordance lines, which provide a comprehensive index of lexical units used in Corpus-22 and Corpus-23.
The analysis was conducted using KWIK technique of the LancsBox software. Studying concordance lines assists in clarifying the instances in which a particular word or phrase appears in context within a text corpus. It also provides valuable insights into the usage, meaning, and collocation of words or phrases within a given corpus formulated based on numeric findings.
Keywords put in context demonstrate a profound explanatory basis and lead the researcher towards objective conclusions. A subset of results received from the KWIK analysis is presented in Table 3. we will return everything to its place strangers here. This is what the people's "united". The seventh day of this terrible were used. Take them home. Ukrainians!
Every will make our cities destroyed by the many other towns and villages, which
the of our people who will persecute the
their land and will not give the victory. Except for the truth. Except for
world for the sake of Ukraine and be destroyed everywhere. They will not
have live. The war must be stopped and does not know how to live in war war invader invader invader invader invader peace. peace peace. peace peace.
is. This is what the people of began. A war we feel the same should know: they will not get anything better than any city in Russia. Enerhodar.
managed to capture, are being held
hostage. to the end. On the third day, a single piece of land. Not a Except for the tranquillity we want to even more. Neutral Switzerland has
supported EU
They will have no food. They will restored as soon as possible. Today was,
Today I held a meeting with the
The obtained findings align with the results taken from the quantitative analysis. As expected, the President stresses the illegal and terroristic nature of the Russian invasion and fully supports Ukrainian defenders and civilians. He encourages people to protect their homeland and truly believe in justice and the upcoming victory. Furthermore, the enemy soldiers are addressed directly without any rude rhetoric but only with a disapproving tone. The theme of peace is highlighted to be the highest moral and stability value which is consistent with the concept of truth, EU values, and prosperity. Overall, the analysis suggests that Zelenskyy’s addresses are characterised by a strong sense of inspiration, strategic communication, and support of Ukrainian citizens, portraying them as heroes fighting for their homeland.
Collocations. As our final approach to the analysis of our corpora, we utilized the GraphColl tool to study the collocates of the lexeme war and provide an explanation of the main themes available in the target corpora. Analysis of collocates identifies the meaning they are put in. The results of the collocation analysis are shown in Figure 1 and Figure 2, which visualize the collocates:
The findings reveal several similarities in the lexical patterns of both corpora. The frequency of the node war comprises 313 hits, and it collocates with 113 lemmas in Corpus-22, while Corpus-23 shows the frequency of 72 hits and collocation with only 27 lemmas.
In Corpus-22, the immediate left-hand collocates of the node war include terms like this, after, day, world, end, days. Among the right-hand collocates are lemmas like we, against, our, Ukraine, they, people. In Corpus-23, the left-hand collocates of war include terms such as russia, full-scale, russian, during, first, year, crimes, and the right-hand collocates contain the lemmas we, all, Ukraine, who.
The most statistically significant collocates in Corpus-22 include against, after, Ukraine, day, world. To show more statistical values, we include a short description of these lemmas. The node war combines with lemma against 44 times out of 313 (7.23) in 32 texts out of 60, e.g., Another night of Russian’s full-scale war against us, against the people, has passed. The lemma after shows its presence with the node war 29 times out of 313 in 22 texts out of 60, e.g., My dears, the time will come when we will be able to sleep. But it will be after the war. After the victory. The lemma Ukraine is used with the war node 26 times out of 313 (4.27) in 20 texts: Ukraine never wanted this dreadful war. And Ukraine doesn’t want it (Table 4):
M R R R R R R R R R L R R L L L R R L R R L
In Corpus-23, we may find lemmas russia, full-scale, against, russian, during, Ukraine. The lemma russia occurs 11 times out of 72 (3.89) and in 9 texts out of 37 as in one of the examples: Russia started a full-scale war against us. The node full-scale occurs 10 times out of 72 (3.53), and in 8 texts out of 37. It is illustrated in the following sentence: For the fact that during a year of full-scale war, Spain has stood with us in defense against Russian terror. And the node war collocates with the lemma against 10 times out of 72 (3.18) in 9 texts out of 37 ones, e.g., There is an obvious war of tyranny against freedom (Table 5):
The presence of the collocates such as against, Ukraine indicates a high level of convergence between the target corpora. Both corpora include several collocates revealing negative discourse prosodies, with a more pronounced attitude observed in Corpus-23. Notably, the current war is identified as terrorist, dreadful, brutal, terrible, shameful, the worst war since World War II in Corpus-22. The use of adjectives with highly disapproving meanings demonstrates a more determined stance expressed in Corpus-22 towards the issue of war compared to the previous findings obtained through the keyword analysis. Thus, both target corpora include lexical items that point to the brutality of the war, its scale, the tragedy of the people, and the fact that it is illegal and terroristic.
Dissimilarities can be detected in the usage of the lemmas russia, russian which are among the immediate collocates of the node war in Corpus-23 whereas they do not appear in Corpus-22 among the first 25 collocates (actually they appear on the 31 and 37 positions). Furthermore, lemmas Russia Russian are left-hand collocates having the subject and attribute position in the sentence structure. This suggests the attacking country is presented as the main doer of the conflict and the one to be responsible for it. Similarly, we do not find particle not in any war collocation in Corpus-23 while in the 2022 one, it is quite frequent (6.25). It might be explained by the conflict abruptness during its first month and rejection and disagreement with the situation in general. Similarly, the lemma end (both as a noun and as a verb) is not found in Corpus-23 while in the first war month, it is quite frequent (3.29). The president uses it to show the positive war outcome: The war must end; … to put pressure on Russia to end this war. Noteworthy, lemma Putin does not feature in any of these corpora.
Common themes. The synthesis of the keyword analysis and collocated data of the node “war” revealed common themes that were emphasized in the target corpora (Table 6). Since many narratives intertwine, the division of linguistic data into distinct thematic groups is not straightforward.
The tabulated data reveal both points of convergence and divergence in the war discourses of the contrasted corpora. Despite both corpora having similar discourse prosodies regarding the ongoing military invasion, differences are observed in several aspects. While in Corpus-22 as well as in Corpus-23 the themes of national unity, country defence and invasion are similar, still they include different keywords to approach these topics. A constitutive difference is observed through the shift from emphasising country defenders and military aid to introducing the issues associated with peace concepts and the importance of international support to resolve the conflict.
The tabulated data reveal both points of convergence and divergence in the war discourses of the contrasted corpora. Despite both corpora having similar discourse prosodies regarding the ongoing military invasion, differences are observed in several aspects. While in Corpus-22 as well as in Corpus-23 the themes of national unity, country defence and invasion are similar, still they include different keywords to approach these topics. A constitutive difference is observed through the shift from emphasising country defenders and military aid to introducing the issues associated with peace concepts and the importance of international support to resolve the conflict.
This study presents the findings of a corpus-based comparative analysis of lexical characteristics of the political address delivered in a video format by President V. Zelenskyy since the start of the Russian full-scale invasion into the territory of Ukraine. We use a combination of quantitative (frequency words analysis, keyword analysis, concordance analysis, collocation analysis) and qualitative measures (political discourse analysis for the identification of common themes) across language corpora. These measures guarantee that the words selected for the identification of corresponding themes are frequently used in a chosen number of texts.
Both similarities and differences were detected in the coverage of lexical features of the address within the target corpora, highlighting general trends in political discourse. By applying Simple maths and Cohen’s D statistics we found that both corpora attributed to the defence strategy of the invaded country employing for its coverage different lexemes. In Corpus-22 we could find the items not, war, people, Mariupol, civilians, corridors, Melitopol, humanitarian, residents, against military (actions), troops, but Corpus-23 is characterised by the concentration of lemmas Bakhmut, warrior, brigades, frontline, against, defense, war, brigade, fighting, soldiers, sanctions, forces, lives, life, march. In contrast, the keywords from Corpus-23 primarily focus on achieving the main concepts of peace (e.g., lemmas health, glory, separate, international, cooperation, security, good) as well as on the agents of the conflict (e.g., lemmas fellow, warrior, brigade vs occupier, terrorist).
Based on the analysis conducted using the KWIC technique within the LancsBox software, it can be concluded that the method provides valuable insights into the lexical patterns and usage within Corpus-22 and Corpus-23. Nevertheless, the contextualized presentation of keywords offers a strong explanatory framework, affirming that President Zelenskyy emphasizes the illegal and terroristic nature of the Russian invasion while offering unwavering support to Ukrainian defenders and civilians. His rhetoric encourages citizens to protect their homeland and maintain a steadfast belief in justice and eventual victory. Notably, Zelenskyy addresses enemy soldiers with a disapproving tone, avoiding offensive language, which underscores a measured and strategic communication style.
In the final stage of the analysis, the GraphColl tool was employed to examine the collocates of the lexeme war and to identify the primary themes present in the target corpora. The use of adjectives with highly disapproving meanings (terrorist, dreadful, brutal, terrible, shameful, the worst war since World War II) demonstrates a more determined stance expressed in Corpus-22 towards the issue of war compared to the previous findings obtained through the keyword analysis. Thus, both target corpora include lexical items that point to the brutality of the war, its scale, the tragedy of the people, and the fact that it is illegal and terroristic. However, it also reveals notable dissimilarities in the use of specific lemmas between Corpus-22 and Corpus-23. Interestingly, in Corpus-23, there is a stronger emphasis on attributing responsibility for the conflict to Russia, as reflected by the prominence of the lemmas russia and russian. This shift suggests a deliberate framing of the aggressor as the primary actor in the narrative. Additionally, the absence of certain terms, such as not and end in Corpus-23, compared to their notable presence in Corpus-22, indicates a change in tone and focus. Early discourse appears to reflect shock, rejection, and a hopeful emphasis on resolving the conflict, while later discourse shifts to a more assertive and accusatory narrative. The exclusion of the lemma Putin from both corpora further suggests a strategic choice to de-personalize the conflict and focus on the broader geopolitical context.
The theme of peace is presented as a paramount moral and stabilizing value, consistent with the principles of truth, European Union values, and prosperity. Overall, the analysis highlights that Zelenskyy’s addresses are characterized by inspirational messaging, strategic communication, and an unwavering emphasis on the resilience and heroism of Ukrainian citizens in their struggle to defend their homeland.
To gain deeper insights into the linguistic features of political address across different war periods, it would be valuable to analyse corpora comprising each war year separately with a larger number of data. Furthermore, conducting a comparative analysis of addresses from each period, including the application of corpus linguistic tools and the analysis of political discourse would be highly informative. This would provide a more comprehensive picture of the linguistic choices and discourses employed by the President regarding the studied topic.
Part of this work is supported by the EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia under Grant No. 09I03-03-V01-00118; VEGA/ Multilingualism in different areas of life in today's Bratislava under Grant No. 1/0295/23.
[15] Gulzar, Rahat, Sumeer Gul, Manoj Kumar Verma, Mushtaq Ahmad Darzi and Farzana Gulzar, Sheikh Shueb. “Analyzing the Online Public Sentiments Related to Russia-Ukraine War over Twitter”. Global Knowledge, Memory and Communication, (2023). https://doi.org/10.1108/GKMC-03-2023-0106 [16] LancsBox. “Lancaster University Corpus Tool Box.” Accessed September 12, 2024.
http://corpora.lancs.ac.uk/lancsbox. [17] Lőrincz, Marianna. “A Comparartive Corpus Analysis of the Russian-Ukrainian War Coverage in Eastern and Western English Language News Releases.” Shìdnij svìt 3 (2023): 115–30. https://doi.org/10.15407/orientw2023.03.115 [18] McCarthy, Michael. Explorations in Corpus Linguistics. Cambridge University Press, 2006. [19] McEnery, Tony, and Andrew Wilson. Corpus Linguistics. An Introduction. Edinburgh
University Press, 2005. [20] Olcott, Don. “The Zelensky Files: Leadership Strategies and Practices for University Leaders.” Ukrainian Journal of Educational Studies and Information Technology 11, no. 3 (2023): 136 –55. https://doi.org/10.32919/uesit.2023.03.01. [21] Petiy, Natalia. “Conceptual Metaphors Verbalizing War in Ukraine in Media Discourse.” Věda a perspektivy 30 (2023): 203-214. https://doi.org/10.52058/2695-1592-2023-2(21)-203-214 [22] Pfleger, Sabine. “La construcción simbólico-discursiva de Volodymir Zelensky: la actualización del mito de héroe.” [Volodymir Zelensky's symbolic-discursive construction: the actualisation of the hero myth]. Lengua y Sociedad. Revista de Lingüística Teórica y Aplicada, 21, no.º 1, (2022): 53-72. https://doi.org/10.15381/lengsoc.v21i1.22584 [23] President of Ukraine. Volodymyr Zelenskyy. “Official Website.” Accessed September 8, 2024.
https://www.president.gov.ua/en. [24] Raza, Amber, and Sadia Malik. “The Ideological Media Representation of Putin and Zelensky in BBC Profiles: A Multimodal Critical Discourse Analysis.” Pak. Journal of Media Science 3, no. 2 (2022): 148-68. [25] Rojo, Luisa Martín, and Teun A. van Dijk. “'There was a Problem, and it was Solved!': Legitimating the Expulsion of `Illegal’ Migrants in Spanish Parliamentary Discourse." Discourse & Society 8, no 4 (1997): 523-566. https://doi.org/10.1177/0957926597008004005 [26] Salihoğlu, Yiğit, and Karatepe, Çiğdem. “Investigation of Online News about Ukraine-Russia War: A Corpus-Based Critical Discourse Analysis.” RumeliDE Journal of Language and Literature Studies, 33 (2023): 1171-86. [27] Shuster, Simon. “2022 Person of the year. Volodymyr Zelensky.” Time, December 7, 2022.
https://time.com/person-of-the-year-2022-volodymyr-zelensky/ [28] Yakymchuk, V., & Lopatiuk, N. (2024). Communication strategies and tactics in political speeches of V. Zelenskyy. Germanic Philology. Journal of Yuriy Fedkovych Chernivtsi National University, (835–836), 127–135. https://doi.org/10.31861/gph2022.835-836.127-134