Who Was Done What? A parser-based study of passive voice constructions in media discourse on the Russo-Ukrainian War

Who was done what? A parser-based study of passive voice constructions in media discourse on the Russo-Ukrainian War Master’s Programme in Linguistic Diversity and Digital Humanities General Linguistics Master’s Thesis Author: Natchanun Sanitdee Supervisor: Chingduang Yurayong 2024-07-31 Helsinki Faculty: Faculty of Arts Degree programme: Linguistic Diversity and Digital Humanities Study track: General Linguistics Author: Natchanun Sanitdee Title: Who was done what? A parser-based study of passive voice constructions in media discourse on the Russo-Ukrainian War Level: Master’s degree Month and year: July 2024 Number of pages: ix + 88 + 25 (Appendices) Keywords: passive voice, corpus linguistics, parser, NLP, news media, Russia-Ukraine War Supervisor: Chingduang Yurayong Where deposited: Helda (Digital Repository of the University of Helsinki) Abstract: The Russo-Ukrainian War is considered the biggest attack to Europe and the first full-scale war in Europe since the Second World War. Media and press do not merely serve the purpose of communicating events, but they have been used as the tool to propagate agenda and play a vital role in shaping the perspectives of the audience towards the events. This prompts scholars to investigate language use in news to examine linguistic phenomena underlined by political and ideological currents. This project analyzes subject-verb pairs occurring under passive voice constructions in news corpora. Passive voice constructions play a crucial role in revealing how agency and responsibility are represented, especially in the discourse surrounding war and conflict, and this aspect has been underexplored in previous studies. Datasets used in this study include the Ukraine War corpus (170K), Leipzig news corpora (17M), and English speaking Russian news corpora (210K). Examining passive pairs, proper noun subjects, by-agent phrases, as well as conducting diachronic analysis on active and passive voice, short and long passive, and beand get-passive, the study aims to answer the following research questions: i) How Russia and Ukraine are represented in the Western news media compared to the Russian news media? ii) How their agency and responsibility are delegated, and iii) How the usage of passive voice changes over time? A combination of theoretical linguistic frameworks on passive voice and semantic roles, corpus linguistic methodologies, statistical measures, as well as natural language processing (NLP) methods, are employed. SpaCy parser is used to extract passive subject-verb pairs. Semantic map aids the categorization of words. Topic modeling reveals different topics within a corpus, and t-score and p-value are used as the measurements of collocational strengths. The main findings include the representation of Russia as a malevolent actor in Western media both towards its own people and foreign citizens while Ukraine is represented as victims. The use of euphemism with Russia signals mechanistic dehumanization. In the Russian news, Russia is found to be associated with neutral and positive verbs. Regarding the assignment of responsibility, weapons are found to be used to perform violent actions while human agents are more often associated with restrictive or non-violent actions. The diachronic analysis indicates an increased usage of short and get passives but a gradual decrease of passive voice in general. Acknowledgements Upon the completion of my master’s thesis, I would like to express my gratitude to my family and friends who gave moral support and useful comments. I feel thankful for the help of professors Miestamo Matti and Sinnemäki Kaius, as well as my supervisor, Yurayong Chingduang, who guided me through this extensive journey and contributed to vast improvements in many core aspects of my thesis. I would also like to thank academic advisors from Language Technology track that helped to refine the programming codes. I am particularly grateful to the University of Helsinki for providing the resources, facilities, and a tuition fee scholarship that made this academic pursuit possible. My thesis would not have been completed without CSC’s supercomputer Puhti (https://www.puhti.csc.fi/), which allowed me to experiment and run complex NLP analyses on large datasets almost without any limitations. Last but not least, a special thanks to my colleagues at the university who introduced me to Overleaf, which made organizing and formatting this extensive work much smoother. Thank you all. Contents Acknowledgements iii List of Tables viii List of Figures ix 1 Introduction 10 2 Theoretical Background 12 2.1 Definition ..................................... 12 2.2 Types of passives ................................. 13 2.3 Be passive and get passive ............................ 16 2.3.1 Informality ................................ 16 2.3.2 Functional properties ........................... 17 2.3.2.1 Dynamic-stative contrast ................... 17 2.3.3 Semantic connotations .......................... 17 2.3.3.1 Good or bad reflection ..................... 17 2.3.3.2 Emotional involvement .................... 18 2.3.3.3 Sympathy ........................... 18 2.3.3.4 Agency ............................. 19 2.4 Counting frequencies ............................... 20 2.5 Terminologies ................................... 20 2.5.1 By-agent ................................. 20 2.5.2 Grammatical and logical subject and object ............... 21 2.6 Semantic roles ................................... 21 2.6.1 Agentive VS Instrumental ........................ 21 2.6.2 Undergoer VS Experiencer ........................ 23 2.6.3 Recipient VS Beneficiary ......................... 24 2.7 Focus ....................................... 24 2.7.1 Focus on verbs .............................. 24 2.7.2 Focus on the last element ......................... 25 2.7.3 Focus is on subject of passive sentences ................. 26 2.8 Usage ....................................... 27 2.8.1 Linking sentences ............................. 27 2.8.2 Handling unknown, irrelevant, or self-evident logical subjects . . . . . 27 2.8.3 Maintaining tact ............................. 28 2.8.4 Emphasizing logical objects ....................... 28 2.8.5 Enhancing sentence structure and cohesion ............... 29 2.9 Trend ....................................... 31 iv 3 Datasets 33 3.1 Three main corpora ................................ 33 3.1.1 Ukraine War corpus ........................... 33 3.1.2 Leipzig corpora .............................. 33 3.1.3 Russian sources .............................. 34 4 Methods 35 4.1 NLP methods ................................... 35 4.1.1 Parser ................................... 35 4.1.2 Topic modeling .............................. 36 4.1.3 Semantic map ............................... 36 4.2 Corpus linguistics methods ............................ 37 4.2.1 Collocation analysis ........................... 37 4.2.2 Association measures ........................... 38 5 Results and Analyses 42 5.1 Comparative studies: Ukraine War corpus VS Leipzig corpora ......... 42 5.1.1 Civilian, violence, and restrictions: Significant pairs and categories . . 42 5.1.1.1 Subjects and verbs categorization (Ukraine War corpus) . . . 42 5.1.1.2 Subjects and verbs categorization (Leipzig corpora) . . . . . 45 5.1.1.2.1 Leipzig 2014: Subjects ............... 45 5.1.1.2.2 Leipzig 2014: Verbs ................. 46 5.1.1.2.3 Leipzig 2023: Subjects ............... 47 5.1.1.2.4 Leipzig 2023: Verbs ................. 48 5.1.1.3 Comparison of the passive subject and verb proportion . . . 49 5.1.1.3.1 Subjects ....................... 49 5.1.1.3.2 Verbs ........................ 50 5.1.1.3.3 Total categories ................... 50 5.1.2 Russia as a bad guy: Proper noun representations ............ 51 5.1.2.1 Proper nouns categorization in the Ukraine War corpus . . . 52 5.1.2.2 Who was done what? ..................... 53 5.1.2.3 Proper noun representations through time ........... 54 5.1.2.3.1 Proper noun categories through time ........ 55 5.2 The examination of passive voice in the Leipzig corpora ............ 57 5.2.1 In search of the like-minded ....................... 58 5.2.1.1 Lookup datasets and initial comparison ............ 58 5.2.1.2 The most significant pairs ................... 58 5.2.1.3 Overlapping pairs between the Ukraine War corpus and the Leipzig News corpora ..................... 60 5.2.2 Passive voice through time ........................ 60 5.2.2.1 Active VS Passive ....................... 60 5.2.2.2 Short VS Long passives .................... 62 5.2.2.3 BeVS Get-passive ...................... 64 5.2.3 Ukraine War is sometimes a big deal: Topics in news reports . . . . . . 67 5.3 Conceal and highlight: By-agent and modifier analysis ............. 70 5.3.1 By-agent semantic categories ...................... 70 v 5.3.2 Russia(n) and Ukraine(ian) in three positions: modifier, subject, and agentive phrase .............................. 74 5.3.2.1 Russia and Russian as modifier ................ 74 5.3.2.2 Russia and Russian as subject ................. 74 5.3.2.3 Russia and Russian as agent .................. 75 5.3.2.4 Ukraine and Ukrainian as modifier .............. 76 5.3.2.5 Ukraine and Ukrainian as subject ............... 76 5.3.2.6 Ukraine and Ukrainian as agent ................ 76 5.3.2.7 Interpretation of the results (Ukraine War corpus) . . . . . . 78 5.4 The perspectives from another side: Russian news sources ........... 78 5.4.1 Popular topics (Russian news sources) .................. 79 5.4.2 By-agent and modifier analysis ...................... 81 5.4.2.1 Russia and Russian as modifier ................ 81 5.4.2.2 Russia and Russian as subject ................. 81 5.4.2.3 Russia and Russian as agent .................. 82 5.4.2.4 Ukraine and Ukrainian as modifier .............. 82 5.4.2.5 Ukraine and Ukrainian as subject ............... 83 5.4.2.6 Ukraine and Ukrainian as agent ................ 83 5.4.2.7 Interpretation of the results (Russian news corpora) . . . . . 83 5.4.2.8 Comparison with the analysis from the Ukraine War corpus . 84 6 Conclusion 85 7 Discussions 87 7.1 Limitations of spaCy parser and static embedding models ............ 88 References 90 Appendices 97 Appendix 1: Corpora ............................... 97 Appendix 2: Source codes ............................ 97 Appendix 3: News links from SketchEngine ................... 98 Appendix 4: Topic keywords and scores ..................... 101 Leipzig corpora .............................. 101 Russian news corpora ........................... 105 Appendix 5: Tables and figures .......................... 107 vi List of Tables 1 Statistics of Ukraine War corpus ......................... 33 2 Statistics of Leipzig corpora ........................... 34 3 Statistics of Russian news corpora ........................ 34 4 Contingency table for "sovereignty_threatened" ................. 38 5 The skewness of association score distributions ................. 41 6 Top 10 pairs in the Ukraine War corpus ..................... 42 7 Proportion of subject and verb categories in the Ukraine War corpus . . . . . . 44 8 Proportion of subject categories in the 2014 Leipzig corpus ........... 46 9 Proportion of verb categories in the 2014 Leipzig corpus ............ 46 10 Proportion of subject categories in the 2023 Leipzig corpus ........... 47 11 Proportion of verb categories in the 2023 Leipzig corpus ............ 48 12 Proper noun representations in the Ukraine War corpus ............. 52 13 Proper nouns of interest .............................. 55 14 The statistics of the Ukraine War corpus and the 2014 and the 2023 Leipzig corpora ...................................... 58 15 Top 10 subject-verb pairs in the 2014 Leipzig corpus .............. 58 16 Top 10 subject-verb pairs in the 2023 Leipzig corpus .............. 59 17 Significance scores of the Ukraine War corpus and the 2014 and the 2023 Leipzig corpora ...................................... 59 18 Active and passive voice sentences and verbs in the Leipzig corpora, as well as the Ukraine War corpus .............................. 60 19 Percentage of by-agent or long passives in Leipzig corpora ........... 63 20 Percentage of beand getpassives pairs in the Leipzig corpora ......... 65 21 Statistics of passive voice sentence sub-corpora ................. 67 22 The sampled 20 pairs of by-agent phrases with the corresponding subject-verb pairs ........................................ 70 23 Semantic roles of by-agent phrases with frequencies in the parentheses . . . . . 71 24 Categories of by-agent in the Ukraine War corpus ................ 71 25 Human agents in by-agent phrases ........................ 72 vii 26 Non-human agents in by-agent phrases ...................... 73 27 Russia and Russian as modifier .......................... 74 28 Russia and Russian as subject ........................... 75 29 Russia and Russian as agent ........................... 75 30 Ukraine and Ukrainian as modifier ........................ 76 31 Ukraine and Ukrainian as subject ......................... 76 32 Ukraine and Ukrainian as agent .......................... 77 33 Statistics of Russian news corpora ........................ 78 34 Top 10 significant pairs in English speaking Russian news sources ....... 79 35 Statistics of Russian passive voice sub-corpora .................. 79 36 Topic keywords and scores of the Russian news sources ............. 80 37 Russia and Russian as modifier .......................... 81 38 Russia and Russian as subject ........................... 81 39 Russia and Russian as agent ........................... 82 40 Ukraine and Ukrainian as modifier ........................ 82 41 Ukraine and Ukrainian as subject ......................... 83 42 Ukraine and Ukrainian as agent .......................... 83 43 Topic keywords and scores in the Leipzig corpus 2014 ............. 101 44 Topic keywords and scores in the Leipzig corpus 2015 ............. 101 45 Topic keywords and scores in the Leipzig corpus 2016 ............. 102 46 Topic keywords and scores in the Leipzig corpus 2017 ............. 102 47 Topic keywords and scores in the Leipzig corpus 2018 ............. 103 48 Topic keywords and scores in the Leipzig corpus 2019 ............. 103 49 Topic keywords and scores in the Leipzig corpus 2020 ............. 104 50 Topic keywords and scores in the Leipzig corpus 2023 ............. 104 51 Topic keywords and scores in the Arctic corpus ................. 105 52 Topic keywords and scores in TASS corpus ................... 105 53 Topic keywords and scores in the Moscow Times corpus ............ 106 54 Proper nouns in the Ukraine War corpus ..................... 112 55 Proper noun occurrences by years in the Leipzig corpora ............ 113 55 Proper noun occurrences by years in the Leipzig corpora ............ 114 56 Common pairs in the Ukraine War corpus and the 2014 and 2023 Leipzig corpora116 57 The transformation of topic keywords and scores ................ 117 58 Topic keywords and scores (2014-2017) ..................... 118 59 Topic keywords and scores (2018-2023) ..................... 119 60 Anglo-Saxon and non Anglo-Saxon media .................... 121 viii List of Figures 1 Dependency tagging in spaCy .......................... 36 2 The histogram of the p-value of t-score in the 2023 Leipzig corpus ....... 41 3 Semantic categories of passive subjects and verbs in the Ukraine War corpus . . 43 4 The comparison of passive subject categories .................. 49 5 The comparison of passive verb categories .................... 50 6 Proper noun categories in the Ukraine War corpus ................ 53 7 Proper noun categories in the Leipzig corpora .................. 56 8 Percentage of passive sentences and verbs in Leipzig corpora .......... 62 9 Dependency parse tree of a by-agent construction with modifier ........ 63 10 Dependency parse tree of a by-agent construction with compound word . . . . 63 11 Percentage of short passive in Leipzig corpora .................. 64 12 The dependency parse trees of be passive .................... 65 13 The dependency parse trees of get passive .................... 65 14 Percentage of get-passives in the Leipzig corpora ................ 66 15 Popular topics in the Leipzig corpora ....................... 69 16 Parsed text by a small model, en_core_web_sm ................. 89 17 Parsed text by a transformer model, en_core_web_trf .............. 89 18 Semantic categories of passive subjects in the 2014 Leipzig corpus . . . . . . . 107 19 The histogram of the p-value of t-score, the p-value of chi-squared test score, and the p-value of Fisher’s exact test score in the Ukraine War corpus and the 2014 and the 2023 Leipzig corpora ........................ 108 20 Semantic categories of passive verbs in the 2014 Leipzig corpus ........ 109 21 Semantic categories of passive subjects in the 2023 Leipzig corpus . . . . . . . 110 22 Semantic categories of passive verbs in the 2023 Leipzig corpus ........ 111 23 The occurrences of the Ukraine War proper noun categories in the Leipzig corpora115 24 Semantic categories of agentive by-phrases in the Ukraine War corpus . . . . . 120 ix Be passive and get passive 2.3 Be passive and get passive The unique properties of GET PASSIVE attract many scholars to examine them. There are three main views towards GET PASSIVE. Firstly, they are seen as informal, colloquial, and inelegant. Secondly, they cannot be used entirely interchangeably with BE PASSIVE. Lastly, they portray distinctive connotations. 2.3.1 Informality GET PASSIVE is seen as a trait of spoken language that are found to be used in colloquial and informal texts (Biber & Gray 2016;Xiao et al. 2006; cited in Plechácková, Jana 2007;Leech & Svartvik 2002: 346; Zúñiga & Kittilä 2019: 100). On these grounds, Di Ferrante (2023) posits that GET PASSIVE does not substitute BE PASSIVE. Example 16 and Example 17 below prove that GET PASSIVE cannot be used interchangeably with BE PASSIVE in a formal context (Example 17), but the choice becomes arbitrary in an informal environment (Example 16). CAUSATIVE CONSTRUCTION:GET/HAVE SOMETHING DONE, where GET is used with PAST PARTICIPLE with the sense of PASSIVE VOICE, helps to illustrate this observation. (16) [INFORMAL] a. A car was washed by a man. b. A man got a car washed. c. A car got washed by a man. (17) [FORMAL] a. A law was passed by the Cabinet. b. ?The Cabinet got a law passed. c. ?The law got passed by the Cabinet. The examples above demonstrate the transformation between BE PASSIVE,GET CAUSATIVE, and GET PASSIVE in order to examine the use of GET PASSIVE in a FORMAL and INFORMAL contexts. Even though GET CAUSATIVE carries a little different sense than BEand GET-PASSIVE in that CAUSATIVE CONSTRUCTION presume the action to be executed by someone else rather than the SUBJECT,Example 16 is totally interchangeable due to the informality of language use. However, among Example 17 with a formal context, GET PASSIVE sounds unnatural and incompatible. This proves that GET PASSIVE is commonly and naturally used in an INFORMAL context and cannot substitute BE PASSIVE in a FORMAL context. 16 (Back to Contents) Be passive and get passive Chappell (1984: 169) offers a historical and more complex analysis. Chappell speculates that earlier grammarians in the 19th and early 20th century tend to view GET PASSIVE as vulgar and colloquial (see Rice 1932 cited in Chappell 1984). Later grammarians, such as Jespersen and Curme, start to characterize the difference between GETand BE-PASSIVE, though the colloquial sense still persisted. 2.3.2 Functional properties 2.3.2.1 Dynamic-stative contrast Jespersen (1949) and Curme (1931) find that GET PASSIVE encodes a shift of state while BE PASSIVE simply entails a state or condition. In other words, GET PASSIVE is used to express a DYNAMIC-STATIVE contrast between PAST PARTICIPLE and ADJECTIVE. Outside of this usage, Jespersen and Curme conclude, GET PASSIVE carries a colloquial sense. Jespersen (1949 cited in Chappell 1984) provides an example where GET is able to distinguish STATE from TRANSITION. (18) a. At that time, he was not married. [BE PASSIVE] b. He got married in 1920. [GET PASSIVE] In Example 18, auxiliary GET is able to distinguish married used as ADJECTIVE signifying a STATE in the first sentence from married as PAST PARTICIPLE in Example 18a marking a TRANSITION from being unmarried. 2.3.3 Semantic connotations Scholars often associate GET PASSIVE with good or bad reflection, emotional involvement, sympathy, and agency. The first three connotations can be grouped as PERSONAL connotations. 2.3.3.1 Good or bad reflection Hatcher (1949 cited in Chappell 1984) posits that GET is used in only two circumstances: to signify the feeling of having either fortunate or unfortunate consequences for the subject. Lakoff (1971 cited in Chappell 1984) has a similar view that GET is frequently used to reflect the ATTITUDE whether the events are ‘good or bad’ or reflect ‘well or poorly’ on the subject. This connotation can also be called EVALUATIVE ATTITUDE. This type of connotation may be transferred from the use of GET TO BE in ACTIVE voice. (19) a. We are here. [BE ACTIVE VOICE] b. We get to be here. [GET ACTIVE VOICE] (Back to Contents) 17 Be passive and get passive (20) a. Jane was invited to the party. [BE PASSIVE VOICE] b. Jane got invited to the party. [GET PASSIVE VOICE] Example 19b uses GET TO BE in ACTIVE VOICE to reflect that get to be here is either good or bad for a speaker or a writer while BE PASSIVE in Example 19a is more neutral and declarative. This EVALUATIVE ATTITUDE connotation of GET PASSIVE is carried through to PASSIVE VOICE as seen in Example 20b, where the fact that Jane got invited to the party is either good or bad for a speaker or a writer. BE PASSIVE in Example 20a, on the other hand, does not carry such connotation. 2.3.3.2 Emotional involvement In addition, Hatcher and Lakoff also discover an interesting property of GET PASSIVE that it conveys the notion of ‘responsibility’ or ‘active involvement’ of the subject. Lakoff points out the OBJECTIVE-AFFECTIVE contrast between BE and GET PASSIVE as follows: “If we think of... an item on a news program... (where a reporter will at least try to present the appearance of (the) objectivity toward the news being reported), GET will be very odd, and BE normal, indicating that the speaker has no feelings about what took place...” (21) a. My cache of marijuana got found by Fido the police dog. b. A cache of Marijuana was found by Fido the police dog. In Example 21a, the sense of the speaker’s involvement in the action and his unhappiness with it are very clear. Lakoff concludes that GET PASSIVE often suggests the active involvement, emotional, or otherwise, of the superficial subject... (1971:160). Therefore, it can be assumed that GET PASSIVE is used to convey SUBJECTIVITY. 2.3.3.3 Sympathy The first two connotations – reflective attitude and emotional involvement – assumes that something is either good or bad for a writer or a speaker, but when they feel bad about something, GET PASSIVE is used to convey SYMPATHY. Chappell (1984) uses the following example to explain this distinction and not included in Lakoff (1971)’s terminology. (22) Che Guevara got assassinated. 18 (Back to Contents) Be passive and get passive In Example 22 and Example 23, a subject and a speaker are not the same person, so subject’s EMOTIONAL INVOLVEMENT cannot be assumed. In this case, Chappell proposes that GET PASSIVE allows a speaker to make an INFERENCE of a NEGATIVE nature about the subject, and this links back to the good and bad reflection mentioned in Section 2.3.3.1 but on the subject, instead of the speaker. Some similar examples can be commonly found in news media as follow: (23) a. People got killed. b. People got injured. Alternative views see GET PASSIVE convey only NEGATIVE connotations in the written language (Xiao et al. 2006) or are NEUTRAL with no adversitive effect on the SUBJECT in a spoken data (Villalibre 2015). 2.3.3.4 Agency In GET PASSIVE,SUBJECT is perceived as having more “control, instigation or responsibility” in the “resulting situation” than BE PASSIVE where SUBJECT is purely an UNDERGOER (Chappell 1984;Zúñiga & Kittilä 2019: 100). GET PASSIVE, therefore, implies that SUBJECT has a certain level of AGENCY that can affects the end result. Consequently, Chappell suggests that if the speaker [or the writer] considers the subject an “innocent victim” of circumstances, and if the subject is thought of as “completely” under the control of another person, and consequently has no choice but to do whatever is desired by AGENT, then GET PASSIVE is NOT appropriate, and BE PASSIVE must be used instead. (24) a. Half of the population of [Cambodia] WAS / ?GOT systematically annihilated under the Pol Pot regime. b. Vietnamese women and children WERE / ?GOT massacred in the My Lai offensive. c. Seven slaves WERE / ?GOT bought. d. Germany WAS / ?GOT forced to surrender. The use of BE PASSIVE in Example 24 is more appropriate as the sentences suppose the subjects Half of the population of Cambodia and Vietnamese women and children are innocent victims, and Seven slaves and Germany do not have any control over the outcomes. However, it can be observed that this connotation only applies to ANIMATE or, more strictly, HUMAN entities because INANIMATE entities do not have AGENCY in the first place. By looking back together with the first three connotations, if GET PASSIVE is used in Example 24, it can mean that a speaker or a writer intends to express the FEELING of sorrow and the SYMPATHY that the situations are unfortunate. (Back to Contents) 19 Counting frequencies 2.4 Counting frequencies Many previous studies on PASSIVE VOICE have been conducted to observe the frequencies of its usage, either by counting manually or using part-of-speech taggers like CLAWS (Garside & Smith 1997). Di Ferrante (2023) recognizes the challenges in extracting all types of passives, which lead many linguists to resort to manual counting methods, resulting in a limited corpus size and generalizability issue of the findings. More robust and customizable modern tools, such as text parsers, enable more flexibility to capture various types of PASSIVE based on POS (partof-speech) and dependency tagging features; however, these tools do require a certain level of a programming skill. Another robust and effective method may involve using machine learning to train a model to classify PASSIVE VOICE constructions. The counting of PASSIVE VOICE frequencies varies in terms of types, units, and measurements. More typical passives like the BE-PASSIVE are generally included, while less typical forms like BARE PASSIVE and COMPOUND PASSIVE are often excluded due to challenges in extracting. Some studies analyze the total number of clauses or only the past participles in passive constructions, while others consider the entire passive verbal phrases. Units of measurement, even normalized ones, can cause difficulties in cross-study comparisons. While ‘per million’ is widely recognized as the standard measurement of word frequencies, many scholars prefer ‘per 100k’ or ‘per thousand’ for their datasets. 2.5 Terminologies In the past studies of passives, several related terms are used, and it is necessary to explore these terms and set the understanding for the later usage throughout this study. There are two main groups of passive-related terms: BY-AGENT, as well as GRAMMATICAL and LOGICAL SUBJECT and OBJECT. 2.5.1 By-agent The most diverse term in the grammatical division refers to BY-AGENT used by Hundt (2004), for example. BY-AGENT consists of preposition BY +AGENT or DOER. AGENT denotes “the source of action” (Anderson 1998) and comes after BY in a BY-AGENT prepositional phrase. (25) The man was injured by the police. Comrie (1988) and Keenan & Dryer (2007: 342) call this component AGENT PHRASE while Katz (2006) comes up with more details as PROPOSITIONAL AGENT PHRASE. The most detailed terminology could be WITHor BY-AGENTIVE PREPOSITIONAL PHRASE. 20 (Back to Contents) Semantic roles When BY-AGENT is present, the whole clause is called AGENTFUL PASSIVE by Anisfeld & Klenbort (1973) and AGENTIVE CLAUSE by Areibi & Al-Mijrab (2012). This links to the length-based terminology: FULL PASSIVE and LONG PASSIVE (Miller 2016). On the contrary, in a similar manner, when BY-AGENT is not present, Miller uses SHORT PASSIVE. This AGENTLESS PASSIVE CLAUSE is called TRUNCATED PASSIVE or DELETED AGENT STRUCTURE by Homan (1989),NON-AGENTIVE constructions by Chappell (1984), and AGENTLESS PASSIVE by Comrie (1988) and Areibi & Al-Mijrab (2012). 2.5.2 Grammatical and logical subject and object SUBJECT and OBJECT in PASSIVE clauses can be categorized based on grammatical and semantic criteria. LOGICAL SUBJECT and OBJECT are identified by their semantic roles, while GRAMMATICAL SUBJECT and OBJECT are identified by their syntactic positions within the clause. Consider the following examples: (26) a. The army destroyed the bridge. [ACTIVE VOICE] b. The bridge was destroyed by the army. [PASSIVE VOICE] In ACTIVE clause, such as Example 26a, there is no repositioning of the SUBJECT and VERB, making the terms straightforward: the army is both GRAMMATICAL and LOGICAL SUBJECT, and the bridge is both GRAMMATICAL and LOGICAL OBJECT. However, in Example 26b,the bridge becomes GRAMMATICAL SUBJECT while remaining to be LOGICAL OBJECT, and the army is LOGICAL SUBJECT. PASSIVE clauses do not have GRAMMATICAL OBJECT because they do not follow the SUBJECT-VERB-OBJECT (SVO) structure typical of ACTIVE clauses. 2.6 Semantic roles LOGICAL SUBJECT and GRAMMATICAL SUBJECT in passive voice clauses can have different semantic roles. Three similar pairs of semantic roles are presented: AGENTIVE and INSTRUMENTAL,UNDERGOER and EXPERIENCER, and RECEIPIENT and BENEFICIARY. 2.6.1 Agentive VS Instrumental The most typical semantic role of LOGICAL SUBJECT of passive clauses are AGENTIVE and INSTRUMENTAL. The semantic roles of the component in PREPOSITIONAL PHRASE in PASSIVE clauses, which follow the propositions BY or WITH, can be classified into two main types: AGENTIVE and INSTRUMENTAL.Quirk et al. (1989) offer the definitions that AGENTIVE refers to the (Back to Contents) 21 Semantic roles ANIMATE being instigating or causing the happening denoted by VERB, and INSTRUMENT represents the entity, generally INANIMATE, which an AGENTIVE uses to perform or investigate a process. With the creative and figurative style, many language users may argue with Quirk et al.’s (1989) definitions that AGENT can also be INANIMATE entities as in The paper is burnt with fire.Anderson (1998) provides a concise yet accurate description of AGENT as “a source of the action”. (27) a. A man was arrested by an authority. b. A man was arrested with a cuff. An authority in Example 27a has an AGENTIVE semantic role as a performer of action arrest, and a cuff in Example 27b takes an INSTRUMENTAL semantic role because it is used as a tool to perform the action. APASSIVE clause can have both AGENTIVE and INSTRUMENTAL prepositional phrases as in: (28) A man was arrested by an authority with a cuff. The order of an AGENTIVE before an INSTRUMENTAL is more sensible than the other way round as DOER is mentioned before TOOL. It can be observed from Example 28 that AGENTIVE are used with the preposition BY, and INSTRUMENTAL follows the preposition WITH. AGENTIVE in many cases can also be an INANIMATE entity that carries the semantic role as the source of action as in the example below. (29) The remains were brought ashore by the waves. The waves is not a living entity but is the one that brought the remains to the shore. The choice to use BY or BY often depends on the intended meaning or the norm, which can differ from the typical use of BY for AGENTIVE and BY for INSTRUMENTAL criteria. (30) The remains were brought ashore with the waves. In this example, using the preposition BY shifts the semantic meaning of the waves away from the typical AGENTIVE role towards a more LOCATIVE role, specifying the location or means. Keenan & Dryer (2007: 342) note that the agent phrase marker [“by”] in English is independently used with LOCATIVE force. The prepositions BY and WITH can be used to indicate other semantic roles, such as locative, as seen in Example 30. These uses may be less related to passive voice constructions, which typically focus on the action or the source of the action. 22 (Back to Contents) Semantic roles 2.6.2 Undergoer VS Experiencer Another pair of semantic roles highly related to passive voice are UNDERGOER and EXPERIENCER.UNDERGOER denotes an entity affected by the action, also commonly referred to as PATIENT, while EXPERIENCER is an entity that feels or perceives something. UNDERGOER is inherent to the GRAMMATICAL SUBJECT of PASSIVE clauses. EXPERIENCER, on the other hand, can be found in both GRAMMATICAL and LOGICAL SUBJECT. (31) a. The decision was made. b. The sound waves were felt by all residents. c. Villagers were frightened by the storm. In Example 31a,the decision is UNDERGOER affected by the action to make. The latter two examples show demonstrate EXPERIENCER in two positions. The EXPERIENCER in Example 31b is all residents who feels the sound waves, and it is villagers in Example 31c who feel frightened. EXPERIENCER in Example 31b falls on BY-PHRASE while in Example 31c, it pertains to GRAMMATICAL SUBJECT. The third scenario where the GRAMMATICAL SUBJECT is EXPERIENCER occurs when SPECIAL VERBS are used. This group of verbs includes verbs like tire, bore, embarrass, disturb, scare, convince, disappoint, and satisfy, which can manifest in three ways: 1. In an ADJECTIVAL GERUND form functioning as COMPLEMENT encoding STATE,QUALITY, or CONDITION, of an entity as in It is interesting. and The food is satisfying. 2. In an PAST PARTICIPLE form signifying FEELING or EMOTIONAL STATE of an entity as in I was impressed by the show. and There is a frightened child in the room. 3. As a TRANSITIVE VERB as in The story convinced me. and The audience excites my speech. When used in passive voice, SPECIAL VERB assigns EXPERIENCER semantic role to the GRAMMATICAL SUBJECT. (32) All residents were disturbed by the sound waves. Similar to Example 31b,all residents in Example 32 above is also an EXPERIENCER, but the semantic role is assigned to SUBJECT position, instead of OBJECT, due to the use of SPECIAL VERB to disturb. EXPERIENCER can be identified with PERCEPTION VERB like hear, see, feel, and enjoy; however, PERCEPTION VERB can be used figuratively, and the context needs to be taken into account when determining a semantic role. (33) a. Economic benefits will be felt by all Brazilian society. (Back to Contents) 23 Focus b. The higher balances were predominantly enjoyed by midand high-income families. In Example 33,PERCEPTION VERB feel and enjoy are used metaphorically to actually signify that an entity is benefited from the economic event. Therefore, all Brazilian society and midand high-income families are considered BENEFICIARY, not EXPERIENCER. BENEFICIARY, together with its similar role, RECIPIENT, is explained in the following section. 2.6.3 Recipient VS Beneficiary RECIPIENT is semantically close to BENEFICIARY, but both have a slightly different property. Kittilä (2005) describes the distinction between the RECIPIENT and BENEFICIARY as follows: “The role of RECIPIENT was defined on the basis of events which inherently involve an entity that receives a thing transferred to its sphere of control. BENEFICIARIES, in turn, benefit from events without receiving anything concrete.” Take for example: (34) a. A family was offered a house. b. A family was granted asylum status. A family in Example 34a is a RECIPIENT because it was given a CONCRETE entity, a house, but in Example 34b,a family has a BENEFACTIVE semantic role as it benefits from receiving asylum status, which is not a CONCRETE entity. 2.7 Focus There are three main perspectives regarding what PASSIVE clauses focus on differently compared to the ACTIVE ones: verbs, last element, and GRAMMATICAL SUBJECT. 2.7.1 Focus on verbs The first viewpoint sees that passive voice emphasizes VERB but as a RESULTATIVE STATE, not the ACTION itself. McKerrow (1978 cited in Homan 1989) makes an interesting statement about the use of passive voice in everyday language as follows: “If in ordinary conversation we use the phrase the boy was kicked by a horse, we are not thinking, as a rule, of the action of kicking, but of the hurt done to the boy. In fact, consideration will show that the use of the PASSIVE construction in ordinary speech is almost 24 (Back to Contents) Focus restricted to occasions when what really concerns us is the condition of something or other due to something else, and not the action itself.” On these grounds, Homan concludes that ACTIVE and PASSIVE representations of a proposition do not mean the same thing; if the passive voice approximates any other GRAMMATICAL FORM, it is more likely an ADJECTIVAL STATE of being. Haspelmath et al. (2004) and Zúñiga & Kittilä (2019: 43) specifically refer to this type of passive where AGENT is completely backgrounded as AGENTLESS RESULTATIVE constructions. Berry (1975: 160) provides a more nuanced distinction that if a speaker decides to leave the ACTOR implicit, the PROCESS or the VERB is emphasized. Conversely, if an ACTOR is made explicit by the use of AGENTIVE phrase, a speaker or a writer intends to emphasize the ACTOR. 2.7.2 Focus on the last element Actually, according to the second group of scholars, the sentence The boy was kicked by a horse. that McKerrow uses may not be the best example to exemplify the FOCUS of passive voice because this group of scholars sees that the emphasis of PASSIVE clauses, in fact, of any sentences, is “at the end”. Therefore, for a PASSIVE clause that ends with BY-AGENT, the FOCUS should fall at the BY-AGENT. Anisfeld & Klenbort (1973) analyze passive voice sentences based on the PRESUPPOSITION PRINCIPLE (Halliday & Matthiessen 2013;Chomsky 1972) and the intonation of speech of passive voice. Anisfeld & Klenbort come to the same conclusion that the emphasis of an AGENTFUL passive sentence is on the BY-AGENT. The example sentence is as follow: (35) The principal had been angered by the parent. In this sentence, the FOCUS is at by the parent, and the rest that comes before is PRESUPPOSITION. Regarding the intonation, Anisfeld & Klenbort observe that under normal circumstances, the last word in each of these PASSIVE sentences receives major phonetic stress. This observation conforms to what Chomsky (1972) proposes that the phrase containing the intonation center (which usually receives major phonetic stress) be considered the assertional FOCUS of the sentence and PRESUPPOSITION of the sentence be defined as the proposition resulting from the replacement of the FOCUS by a variable. Chomsky uses two sentences below to further illustrate. (36) a. Did John give the book to Bill? b. Did John give Bill the book? (Back to Contents) 25 Trend Although the work of Seoane & Loureiro-Porto (2005) provide a useful framework to analyze the usage of PASSIVE VOICE, the size of corpus is limited to only 110k each across the three time periods (1905-1925, 1960-1975, and 1985-1990), and the texts are sampled from scientific articles only. Seoane & Loureiro-Porto note that the decline might be rooted in events and dynamics that are sociolinguistic in nature. Within the last of the three periods of interest in Seoane & Loureiro-Porto’s (2005) work, Homan (1989) mentions that PASSIVE VOICE is an accepted form used in academic writing and also for other purposes to achieve a neutral and objective tone. Homan states that in general, people used PASSIVE VOICE without asking a question; although there were some critical analyses on the topic of AGENT omission, there was no clear opposition. Not for too long after Homan’s study, there are many accusations of PASSIVE VOICE that discourage its usage. Some see PASSIVE VOICE as a tool that makes the situations “less severe” than they actually are, “increases perception of victim responsibility”, and “decreases perception of perpetrator responsibility” (Abramski et al. 2024 citing Bohner 2001;Henley et al. 1995; Northcutt et al. 2019). Studies in the context of violence against women (VAW) (see Katz 2006) point out that PASSIVE VOICE is used to ‘exclude the the culprits’ and to ‘emotionally distance the readers’ away from the events. Apart from the “reader” side, Reilly et al. (2005: 191) state that such AGENTLESS actions also distance the “writer or speaker” from the text. Kahn (1992) also refers to PASSIVE VOICE as a “soulless” voice. Bohner (2001) criticizes the use of PASSIVE VOICE as an indicator of responsibility attribution to the victim and considers other mechanisms like nominalizations or paraphrases to be more reliable. Outside of academia, this trend is also prevalent. There are many posts on online platforms asking for suggestions to avoid using PASSIVE VOICE or to gauge the opinions why people dislike it. In Reddit (https://www.reddit.com/), for example, these topics are commonly found in r/writing, r/grammar, and r/ENGLISH channels. Around the same period of time in the late 19th century and early 20th century, some scholars made a statement to defend PASSIVE VOICE. This opposite force of opposition can be observed as early as 1969 in DeVito’s (1969) work. DeVito states that to recommend that PASSIVE be avoided because they are more difficult to understand is a “gross simplification” of the facts. Ferreira (2021) also writes an article In Defense of the Passive Voice to point out the necessity and advantages of PASSIVE VOICE and eloquently addresses each accusation made towards PASSIVE VOICE. This defensive wave is also reflected through more balanced views that encourage the use of PASSIVE VOICE as appropriate and with a critical mind (Gilpin & Patchet-Golubev 2000; Williams & Rivers 1983). 32 (Back to Contents) 3 Datasets 3.1 Three main corpora Three corpora are used to analyze passive constructions; Ukraine War corpus, the Leipzig corpora, and the Russian News corpora. 3.1.1 Ukraine War corpus The Ukraine War corpus is a web-crawled corpus compiled by SketchEngine (Kilgarriff et al. 2014) with the following keywords: Russian-Ukrainian War, Russo-Ukrainian War, Ukraine War, and Russias War in Ukraine. Before the compilation, certain parameters are set to ensure a maximum size of the corpus: Size and relevance set to larger size and Max document size and Max cleaned document size set to the maximum of 15000 kB. After the compilation, the corpus is cleaned by removing the HTML snippet <p> and </p>, as well as irrelevant numbers and symbols, such as [20][21][22] mixing in the texts and ˆ in front of the lines ending up with around 170k words. Table 1: Statistics of Ukraine War corpus Corpus #Sentence #Word Word/Sent. Ukraine War 5,269 170,834 32.42 3.1.2 Leipzig corpora Leipzig corpora (2014-2020, 2023) (Quasthoff et al. 2012) are downloaded from the Leipzig corpora collection website in the category News. Because the years 2014 and 2022 mark the start and the major escalation of the Ukraine war, respectively, but news for the year 2022 is not available on the repository website, only news from the years 2014 and 2023 are collected. In the Leipzig corpus, line numbers, lines with less than 10 words and more than 4 numerical digits, as well as lines with a double quotation mark (direct speech) are removed. This leaves 12 and 13 million words in the Leipzig 2014 and 2023 corpora, respectively. The statistics of the Leipzig corpora utilized as datasets are shown in Table 2 below. 33 Three main corpora Table 2: Statistics of Leipzig corpora Corpus #Sentence #Word Word/Sent. Leipzig_2014 756,346 16,859,714 22.29 Leipzig_2015 765,565 17,199,603 22.47 Leipzig_2016 841,467 18,425,587 21.90 Leipzig_2017 836,561 17,910,042 21.41 Leipzig_2018 839,051 17,951,766 21.40 Leipzig_2019 845,091 18,128,565 21.45 Leipzig_2020 846,917 18,342,325 21.66 Leipzig_2023 854,997 18,498,430 21.64 Average 826,347 17,829,303 21.82 3.1.3 Russian sources Three English-speaking Russian news media are used to build Russian news corpora. - TASS (https://tass.com/) - the Arctic (https://arctic.ru/) - The Moscow Times (https://www.themoscowtimes.com/) The three websites are used in SketchEngine (Kilgarriff et al. 2014), a web-crawling tool, to compile language corpora. The compilation method in SketchEngine is different from the Ukraine War corpus because this time the search is based on web URLs, instead of sets of keywords. Other parameters remain the same: to set the maximum size of the corpora to the largest. The statistics of the three Russian News corpora are in Table 33 below. Table 3: Statistics of Russian news corpora Corpus #Sentence #Word Word/Sent. #All Passive Pair #Filtered Passive Pair Arctic 4,705 203,785 43.31 1,614 47 TASS 6,866 219,684 32.00 1,663 30 The Moscow Times 5,571 206,742 37.11 1,695 30 Average 5,714 210,737 37.47 1,657 36 34 (Back to Contents) 4 Methods 4.1 NLP methods 4.1.1 Parser Dependency parsing is an approach to automatic syntactic analysis of natural language inspired by the theoretical linguistic tradition of dependency grammar (Kübler et al. 2009). Syntactic structure is expressed as directed dependencies between words Dependencies are labeled with relations, such as subject nsubj or direct object dobj Dependency relations are represented by arrows pointing from the head to the dependent. Words can also be tagged with part-of-speech (POS) underneath them The goal of a dependency parser is to induce this kind of structure for a given utterance it is typically required that a sentence be tokenized, POS-tagged, and lemmatized (Sohn et al. 2012). SpaCy (Honnibal & Montani 2017) was developed in 2015. It is a state-of-art text parser that enables a simple yet efficient extraction process of linguistic structures. Relying on an arc-eager transition-based system, SpaCy’s dependency parsing algorithm uses parsing transitions to draw a dependency tree from an input sentence. The transition-based system, although may suffer from error propagation (an incorrect prediction of a tag affecting the following ones), performs better than the graph-based system thanks to its rich feature representation leading to more accuracy in frequently occurring relations like subject and object dependents (Kübler et al. 2009). The transformer architecture in SpaCy’s currently largest language model en_core_web_trf provides high-accuracy dependency parsing. The identification of passive voice subject-verb pairs is based on the dependency tag nsubjpass readily available in SpaCy parser (see Figure 1). 35 NLP methods Figure 1: Dependency tagging in spaCy Other extractions involve additional dependency tags; for example, AGENTIVE BY PHRASE relies on agent tag and MODIFIER is captured with amod and compound tags. The extraction methods are explained in details in the responsible sections. 4.1.2 Topic modeling Topic models are algorithms for discovering the main themes that pervade a large and otherwise unstructured collection of documents. The simplest topic model is Latent Dirichlet Allocation (LDA) (Blei 2012). LDA is a traditional statistical probabilistic model based on the probability distributions of words over a corpus (Ylä-Anttila et al. 2022). It is considered a STATIC language model as it is trained to learn “one” fixed embedding for each word in a corpus. Another type of language models is a CONTEXTUAL language model, such as BERTopic employed in this study. The CONTEXTUAL language models have a clear advantage over STATIC models in their capabilities in understanding the SEMANTICS of the same word occurring in different contexts, leading to more coherent and meaningful results. There are many studies that use topic modelling with the texts a specific focus; for example, public policy (Isoaho, Karoliina and Gritsenko, Daria and Mäkelä, Eetu 2021), parliament debate (Magnusson et al. 2018), and climate change (Ylä-Anttila et al. 2022). The use of topic modeling in this study is to find out a variety of topics within general news reports (the Leipzig corpora). By doing this, a specific “topic” in mind is “the Ukraine War”, and the goal is to find out how popular or significant this topic is compared to others. 4.1.3 Semantic map In this project, a semantic map is used to aid in the categorization of words.1ASTATIC word embedding model, specifically Word2Vec, is selected due to its clear representations of word 1The code to generate a semantic map is adapted from Natural Language Processing Demystified (Parker 2022) 36 (Back to Contents) Corpus linguistics methods meanings. This approach involves creating a visual representation of words based on their semantic similarities. The semantic maps are created using a pre-trained STATIC embedding model, Word2Vec, in the Gensim library. The model is trained on the Google News Corpus (around 3 billion words). STATIC word embedding models like Word2Vec generate semantic scatter plots where each word is represented by a fixed vector based on its meaning. These vectors are plotted in a multidimensional semantic space, with words of similar meanings positioned closer together. Because these models create semantic scattered plots based on fixed meanings for each word Jurafsky & Martin (2023: 120), they can return inaccurate coordinates of words on a semantic map. For instance, words with multiple parts-of-speech like place,list,target, and sentence may be inaccurately plotted according to their more common usage as a NOUN without taken into account their contextual meanings that can also be VERB. Despite its limitations, this method remains useful for illustrating semantic clusters. An alternative to STATIC embedding is a CONTEXTUAL embedding model, such as BERT. While CONTEXTUAL embedding models provide more accurate representations of words within specific contexts, the resulting semantic plots can be challenging to interpret for categorization purposes. This complexity arises because BERT handles NAME and PROPER NOUN differently, often causing NAME to dominate the plot area and other words to cluster together. Therefore, the Word2Vec model in the Gensim library is used. The model’s ability to clearly illustrate semantic clusters makes it an effective tool for categorizing words based on their meanings. 4.2 Corpus linguistics methods This project employs various corpus linguistic approaches to assess the association of passive subject-verb collocates. These statistical methods sequentially build upon observed (O) and expected (E) frequencies derived from a contingency table leading to the calculation of chi-squared test score, Fisher’s exact test score, and t-score, culminating in p-values. The p-values from the chi-squared test, Fisher’s exact test, and t-score are compared, and after testing, the p-value of the t-score is used as a main measure to assess the collocational significance in this project due to their balanced distributions. The utilization of these measures for studying word pair occurrence frequencies is inspired by Evert (2004). 4.2.1 Collocation analysis Collocation is the co-occurrence of two words. A stronger collocate means that the pair tends to occur together more often. This relationship is called the collocational strength. This section covers the methods to derive observed and expected frequencies using a contingency table. (Back to Contents) 37 Corpus linguistics methods Observed frequencies are the actual or raw counts of the occurrences whereas expected frequencies are the occurrences that are expected to occur. Expected frequencies are calculated using a contingency table. A contingency table is a table that represents the cross-classification of the collocational scenarios of subject-verb pairs. Four collocational scenarios are counted in the corpus: i) Subject and verb co-occur. ii) Subject is present without verb. iii) Verb is present without subject. iv) Neither subject nor verb is present. The contingency table for each subject-verb pair has a 2-row-and-2-column dimension. Table 4 below is the contingency table for the subject-verb pair sovereignty_threatened from the Ukraine War corpus, with expected frequencies in parentheses: Table 4: Contingency table for "sovereignty_threatened" Threatened (Present) Threatened (Absent) Row Total Sovereignty (Present) 4 (0.015) 0 (3.985) 4 Sovereignty (Absent) 1 (94.511) 1325 (1162.489) 1326 Column Total 5 1325 1330 Expected frequencies are calculated by multiplying the sum of the row with the sum of the column and dividing by the total occurrences of all pairs in the corpus. For instance, the expected frequency where both subject "sovereignty" and verb "threatened" occur together is 0.015, calculated as (4+0)∗(4+1) 1330 . The expected frequency E(O) of the scenario i (in blue) where both subject and verb co-occur is calculated by multiplying the sum of the row with the sum of the column and dividing with total occurrences of all passive pairs in the corpus. For instance, the expected frequency where both subject sovereignty and verb threatened occur together is 0.015, calculated from (4+0)*(4+1)/1330. The same calculation applies to the other three scenarios. 4.2.2 Association measures Evert et al. (2008) defines association scores as a quantitative measure of the attraction between words. The association measures employed in this study is t-score. However, this section also covers the Chi-squared test score and Fisher’s test score in comparison with t-score. The comparison shows that t-score is the most appropriate measure providing a more balanced assessment. The calculation of association measure continues from the expected frequencies and the observed frequencies. The first association measure of collocation is chi-squared test score. Chi-squared 38 (Back to Contents) Corpus linguistics methods test score measures goodness-of-fit for the observed frequencies against expected frequencies. A recommended standard version of the chi-squared test score for co-occurrence data (Evert 2004) can be calculated by squaring the difference between observed frequency and expected frequency (4-0.015)2=15.88, multiplying with a total occurrences (N=1330), and dividing with the multiplication of the expected frequency of scenario iand ix (0.015*1162.489=17.44), which is 1211.03. The calculation formula of the standard chi-squared test score is shown below. Chi-squared Test =(Observed freq. of 𝑖−Expected freq. of 𝑖)2∗𝑁 Expected freq. of 𝑖∗Expected freq. of 𝑖𝑥 A larger Chi-squared score indicates a greater deviation from expected frequencies, signifying statistical significance, often assessed using a p-value. P-value represents the probability of observing a chi-squared test score in the extreme tail of Chi-squared distribution. Given a Chisquared score and a degree of freedom (df), which is 1 for each individual pair, p-value can be calculated based on the chi-squared distribution table, or in this study, by using the stats.chi2.sf function from SciPy module (Virtanen et al. 2001) in Python. When a p-value is less than a certain significance level, which is typically 0.05, the frequency difference is statistically significant. Canning (2014) mentions the limitations of the chi-squared test scores including the fact that this statistical score may not be applicable with a small sample size. Canning provides a rough definition of the small sample size that is when more than 20% of the cells return expected values below 5. In the case of a small sample, the p-value of Fisher’s exact test is often used. Fisher’s Exact Test computes an exact p-value rather than an approximation that is only valid for large samples (Evert 2004). The calculation of Fisher’s exact test score concerns all four occurrence scenarios in the contingency table and is done using the stats.fisher_exact function in the SciPy module (Virtanen et al. 2001) in Python. To assess if the current sample size (the observed co-occurrence of a pair) is adequate for the chi-squared test score to have enough power, Cohen’s w (Cohen 1988) and Power Analysis are applied. Cohen’s w is a measure of the effect size of the chi-sqaures score. The computation of Cohen’s w in the context of a contingency table is done by square-rooting the division of the sum of the Chi-square components (of all four scenarios) with total occurrences. The formula of Cohen’s w calculation is shown below. w= √ √ √ √∑𝑖−𝑖𝑥 (Observed freq. −Expected freq.)2 Expected freq. 𝑁 After that, Cohen’s w is used as an effect size parameter in the POWER ANALYSIS to obtain a necessary sample size. Other parameters include the significance level, which is 0.05, and the (Back to Contents) 39 Corpus linguistics methods power level (to correctly reject null hypothesis), which is typically 0.8. POWER ANALYSIS is conducted in Python using the power module, as well as the solve_power function from the GofChisquarePower class (Seabold & Perktold 2010). Another common association measure used to interpret the significance of co-occurrences is the t-score. The t-score is a measure used in hypothesis testing. To calculate a t-score, the difference between the observed and the expected frequency of scenario iis divided by the square root of the expected frequency of scenario i. The calculation formula of t-score is shown below. 𝑡=Observed freq. of 𝑖−Expected freq. of 𝑖 √Expected freq. of 𝑖 After the three approaches were tested and compared in this project, it was found that the p-value of the t-score is a more appropriate measure to assess the significance of co-occurrences. A vast majority of the p-values of the chi-squared test tends to be very low (p<0.01) while the p-value of the t-score is more evenly distributed. Fisher’s exact test score returns a less drastic distribution of p-value than the chi-squared test score, but the distribution is still highly skewed towards the lower range (negative or left skewed). This uneven distribution results from the fact that the chisquared test and the Fisher’s exact test take in the occurrence statistics from all four scenarios where a large portion of observations fall into scenario ix where neither subject nor verb is present. This returns a high frequency of unusually large chi-squared test scores and extremely low p-values, which might not be representative of meaningful co-occurrences. The t-score, on the other hand, only focuses on scenario i, where both subject and verb co-occur, so it provides a more evenly distributed p-value indicating a more balanced assessment of significance. This aligns with Dennis (1965 cited in Evert 2004), who suggests that t-score is more useful than chisquared test for extracting significant co-occurrences, Lijffijt et al. (2016) who recommend the use of the t-test for comparing word frequencies across corpora, and Evert et al. (2008), whose experiments show that t-score is the best indicator for lexical prepositional-phrases-and-verbs collocations among all association measures. Kilgarriff (2005) concerns that application of the chi-squared test leads to finding spurious results. Table 5 and Figure 2 below reflect the skewness of p-value distributions across three measures and three corpora. The table shows the percentage of subject-verb pairs with p-values falling within the first standard deviation from zero [0,STD] as highlighted in yellow in the histogram, as well as the skewness coefficients. The more positive the skewness coefficient, the more skewed the histogram towards the left side, signifying low values. The comparison of the p-value distributions from the three measures can be found in Figure 19. 40 (Back to Contents) Corpus linguistics methods Table 5: The skewness of association score distributions [0, STD] skewness Corpus t-score Chi sq. test Fisher’s exact t-score Chi sq. test Fisher’s exact Ukraine War 75.97% 97.08% 92.74% 4.16 9.94 8.41 Leipzig 2014 75.45% 91.79% 87.41% 2.48 4.17 4.04 Leipzig 2023 75.54% 92.28% 87.61% 2.57 4.40 4.20 Average 75.65% 93.72% 89.25% 3.07 6.17 5.55 Figure 2: The histogram of the p-value of t-score in the 2023 Leipzig corpus (Back to Contents) 41 Comparative studies: Ukraine War corpus VS Leipzig corpora Category Subjects_Lz23 % Total 100.00 Table 10 shows that subjects in the 2023 Leipzig corpus gradually descend by 1-3% off categorical weights with no major occupants. The top categories are Neutral and Civilian (18.54% and 16.36%, respectively) closely followed by the new category Monetary and Business (12.55%), as well as Law (10.02%). Media, Entertainment, and Technology are found at a little more than 2% than Communication and Death and Danger (8.96%, 6.72%, and 6.23%, consecutively). Nominalizations, as well as Authority, Commute, and Research and Academic account for 5.82%, 4.17%, 3.94%, and 3.86%, respectively. The least identified categories are Location (1.85%) and Names (0.98%). 5.1.1.2.4 Leipzig 2023: Verbs The semantic map of Leipzig 2014 verbs can be found in Figure 22 in the Appendix. Table 11: Proportion of verb categories in the 2023 Leipzig corpus Category Verbs_Lz23 % General Actions used, granted, served, led, given, found, updated, founded, made, discovered, happened, released, done, taken, needed, expected, born, attended, set, hosted, built, completed 53.01 Communication Actions issued, told, called, announced, confirmed, revealed, described, reported, said 13.86 Restrictive Actions closed, arrested, held, charged 11.39 Harmful Actions fired, killed, injured, displaced, stabbed, shot 6.20 Motion raised, reached, dropped, directed, carried, left 5.32 Perception Actions heard, known 4.06 Monetary and Business Act. paid, sold, funded, spent 3.32 Law and Rule Actions indicted, filed, registered, sentenced 2.05 Media, Entertainment, Techrelated Actions played 0.79 Total 100.00 Table 11 shows the percentage of verbs in the Leipzig 2023 corpus. There is a new group of verbs related to Monetary and Business identified in the 2023 Leipzig corpus whereas Disease-related verbs are not present. A majority of verbs are General Action verbs (53.01%). Communicative verbs come in second place at 13.86%. Restrictive verbs account for 11.39% and 6.20% for Harmful verbs. Motion verbs and Perception verbs are found at 5.32% and 4.06%, respectively. Verbs related to Monetary and Business account for 3.32% while verbs related to Law and Rules occupy only 2.05%. Lastly, Media, Entertainment, and Technology verbs are the least commonly identified ones at 0.79%. 48 (Back to Contents) Comparative studies: Ukraine War corpus VS Leipzig corpora 5.1.1.3 Comparison of the passive subject and verb proportion By comparing subjects and verbs between the three corpora, a couple of interesting observations can be made. 5.1.1.3.1 Subjects Figure 4: The comparison of passive subject categories Figure 4 demonstrates the proportion of different passive subject categories in the Ukraine War corpus compared to the 2014 and the 2023 Leipzig corpora. The Ukraine War corpus, being a specific corpus about the war, is distinct with a much higher proportion of subjects in three categories: Civilians (56% compared to 22% and 21% in the 2014 and the 2023 Leipzig corpora, respectively), Location (16% compared to 4% and 2%), and Name (7% compared to 1% in both Leipzig corpora). However, the Ukraine War corpus exhibits a low percentage of subjects related to Law and Governance (1% compared to 10% and 9% in the 2014 and 2023 Leipzig corpora, respectively) and no significant subjects (0%) in the Media, Entertainment, and Tech,Research and Academia,Commute,Monetary and Business, and Disease categories. This may be due to the fact that it is a war-specific corpus as well. (Back to Contents) 49 Comparative studies: Ukraine War corpus VS Leipzig corpora 5.1.1.3.2 Verbs Figure 5: The comparison of passive verb categories In terms of verbs, Figure 5 shows that the Ukraine War corpus has a much higher proportion of Harmful verbs when compared to the 2014 and the 2023 Leipzig corpora (56% compared to 13% and 6%, respectively). Verbs related to Law and Rules are also found more in the Ukraine War corpus (8% compared to 4% and 2% in the 2014 and the 2023 Leipzig corpora, respectively). General action verbs are used distinctively more in the Leipzig corpora (47% for the 2014 and 53% for the 2023), as only 13% is identified in the Ukraine War corpus. The same phenomenon happens in Perception verbs with the occurrence of only 1% in the Ukraine War corpus compared to 4% in both Leipzig corpora. Last but not least, the verb categories that are totally absent in the Ukraine War corpus are Media, Entertainment, and Tech,Disease, and Monetary and Business. 5.1.1.3.3 Total categories To conclude, there are 14 categories of subjects and 10 categories of verbs detected as follow: 50 (Back to Contents) Comparative studies: Ukraine War corpus VS Leipzig corpora Subjects: 1. Civilian 2. Neutral Entities 3. Law and Governance 4. Death and Danger 5. Media, Entertainment, Technology 6. Communication 7. Authority 8. Research and Academia 9. Location 10. Commute 11. Monetary and Business 12. Nominalization 13. Disease 14. Name Verbs: 1. General Actions 2. Restrictive Actions 3. Harmful Actions 4. Communication Actions 5. Law and Rule Actions 6. Perception Actions 7. Motion 8. Media, Entertainment, Technology-related Actions 9. Disease-related Actions 10. Monetary and Business Overall, the Ukraine War corpus demonstrates unique patterns of passive subject and verb categories as it is a war-specific corpus compared to general news corpora like the Leipzig ones. The main characteristics include a higher usage of Civilian,Location, and Name subjects, as well as verbs related to Harm and Law and Rules. Moreover, the Leipzig corpora exhibit a higher diversity of categories having words related to Media, Entertainment, and Tech,Research and Academic,Commute,Monetary and Business, as well as Disease, that are not present in the Ukraine War corpus. The last noticeable trend is that subjects related to Media, Entertainment, and Technology, as well as Monetary and Business, start to rise in their usage in 2023. This overview of how subject and verb categories behave in the Ukraine War corpus compared to news media in general must be taken with a caution that it only represents the filtered subjectverb pairs based on their significance scores and frequencies, not all the words in the corpus. What seems to matter here may be less about subjects and verbs but more about the “individuals” represented in the news media. Consequently, we will divert our focus to analyze the names or proper nouns related to the Ukraine War in the Leipzig corpora during the years 2014-2020 and 2023. 5.1.2 Russia as a bad guy: Proper noun representations Proper noun subjects in the corpus can signify different perspectives related to the conflict. There are five names that show up in the list: Kamardin,Shtovba,Volkov,Urey, and Petrova. The news lines in which the names of individuals appear are in Table 12 below. The close-reading analysis indicates that the representations of these individuals reflect a malevolent image of Russia in political, cultural, humanitarian, and international aspects. (Back to Contents) 51 Comparative studies: Ukraine War corpus VS Leipzig corpora Table 12: Proper noun representations in the Ukraine War corpus Subject_Verb_Pairs News Line Kamardin_given Artyom Kamardin was given a 7-year prison sentence Thursday for reciting verses against Russias war in Ukraine, a tough punishment that comes during a relentless Kremlin crackdown on dissent. Shtovba_sentenced Yegor Shtovba, who participated in the event and recited Kamardins verses, was sentenced to 5 1/2 years on the same charges. Petrova_arrested Petrova was arrested in May 2022 and placed in pretrial detention over a post on Russian social network VK, in which she criticized Russian officials for what the Kremlin insists on calling a special military operation in Ukraine, the lawyer told Russian independent news site Mediazona. Petrova_committed Petrova was committed to a psychiatric facility. Petrova_declared These evaluations are common but in a rare turn, Petrova was declared mentally incompetent. Petrova_escorted Viktoria Petrova is escorted by police for a hearing in a court in St. Petersburg, Russia, Friday, March 3, 2023. Petrova_sentenced Petrova was sentenced to involuntary treatment in a psychiatric facility after she condemned Russian officials for sending troops into Ukraine on social media. Volkov_attacked Leonid Volkov, the former chief of staff for Navalny, who died in a Russian penal colony in February, was attacked with a hammer in the Lithuanian capital of Vilnius on Tuesday. Urey_captured Paul Urey and Dylan Healy, two British aid workers, were captured by Russian forces. Aid worker Paul Urey was captured by Russian forces on 29 April 2022 and died in detention on 15 July 2022. Truss said Urey "was captured while undertaking humanitarian work" and that he was in Ukraine "to try and help the Ukrainian people in the face of the unprovoked Russian invasion." Urey_diagnosed Urey had been diagnosed with a number of chronic diseases, the statement added. These news texts imply that the acts of Russia are destructive both towards its own citizens like Artyom Kamardin and Yegor Shtovba, who recited verses against the war, Viktoria Petrova, who condemned the violence, Leonid Volkov, who is a close ally of the late Russian opposition leader Alexei Navalny, and foreign individuals like Paul Urey, who tried to provide humanitarian aid during the war. 5.1.2.1 Proper nouns categorization in the Ukraine War corpus To find out more comprehensively how different important figures are represented in the news media, further investigation is conducted. In total, there are 61 proper nouns identified in the Ukraine War corpus, and they are categorized into four categories: people,locations,organizations, and terms/concepts.Table 54 in the Appendix shows the proper nouns, their descriptions, and accompanying verbs. The percentage weight is calculated from the frequencies of the proper 52 (Back to Contents) Comparative studies: Ukraine War corpus VS Leipzig corpora nouns presented in passive voice. The nationalities Ukrainian and Russian are under the Locations category because they relate to the countries or refer to the territories whereas the People category only consists of individuals names, not nationalities. According to Table 54, the most dominant category is Locations (50.98%) followed by People (41.83%). Terms/concepts and Organization names appear almost as frequently (3.92% and 3.27%, respectively). Russia occupies a large portion in both location and people categories (30.72% and 28.76%, respectively) while Ukraine comes in second place at 16.34% in location and 5.88% in people category. The disputed areas are minimally mentioned at the rate of 1.31%. Other location names appear 2.61% of all proper nouns. For the people category, apart from the dominant occurrences of Russia and Ukraine,British and American citizens are also in the news in passive voice at 3.92% and 1.31% respectively. Other nationalities take only 1.96%. Figure 6 below illustrates the proportion of proper noun subjects occurring in passive voice construction. Figure 6: Proper noun categories in the Ukraine War corpus 5.1.2.2 Who was done what? Examining verbs that come with proper nouns reveals several interesting points. British and American citizens appear with restrictive verbs (charged, captured, interrupted, arrested) while Ukrainian and Russian subjects occur with a more diverse group of verbs. Violent verbs (killed, poisoned, attacked) exclusively being present with Ukrainian governmental and military officials,Russian activists and advocates, as well as people of other nationalities (except the UK and the US). The same phenomenon can also be observed in the nationality subcategory. The violent verb killed occurs only with Ukrainians, but for Russians, the euphemism swept is used instead. Euphemism is an indirect word or phrase that people often use to refer to something (Back to Contents) 53 Comparative studies: Ukraine War corpus VS Leipzig corpora embarrassing or unpleasant, sometimes to make it seem more acceptable than it really is (Euphemism n.d.). Allan & Burridge (2006) analyze the use of euphemisms in discussing death and killing. They indicate that the use of euphemisms to talk about killing serves to distance individuals from the harsh realities of violence, potentially easing moral qualms. This observation points out that the Russian governmental and military officials, the UK and the US citizens, and Russian nationality are exempted from the use of violent verbs, whereas Ukrainians appear with those verbs and are portrayed as victims. The use of the euphemism swept instead of killed for Russians could be seen as a form of dehumanization. Based on Haslam’s (2012) framework, Russians experience mechanistic dehumanization, with the euphemism swept that abstracts and sanitizes their deaths, removing emotional and individual recognition. Haslam identifies the common theme of previous studies on dehumanization – delegitimization (Bar-Tal 2000), moral exclusion (Opotow 1990;Kelman 1973), moral disengagement (Bandura 2002), values judgements (Struch & Schwartz 1989), and infra-humanization (Leyens et al. 2001) – and concludes that dehumanization serves motivated functions like relief from moral emotions, self-exoneration, and justification for violence. This finding explains the void of violent verbs. Kahn (1992) examines the use of passive voice in the context of animal research and identifies various euphemisms, such as dosed instead of poisoned and processed instead of killed, which is to avoid or even conceal the jarring or unsettling truth of a situation. In addition, the categories Activists and Advocates, as well as Poets and Artists, are only present under Russian nationalities, but none for Ukrainians. Those activists, advocates, poets, and artists are pro-democracy, anti-war, and condemn Russias invasion into Ukraine. Verbs accompanying these categories signify harmful actions (kill, poison, attack) and restrictions (arrest, charge, sentence). This portrays a suppression of creativity and people who call for peace who were mistreated by Russia. The benign image of Russia is further emphasized with a list of international victims including Healy,Urey,Shapps,Shekharappa, and Guemy, who were harassed, threatened, and killed. The number of victims including 9 domestic and 5 international ones also reflects the suppressive and dangerous image of Russia towards its opponents regardless of nationalities. 5.1.2.3 Proper noun representations through time A diachronic analysis is conducted to examine how the proper noun representations in terms of the categories and accompanying verbs change over time in the Leipzig corpora (2014-2020, 2023). Based on the proper nouns found in the Ukraine War corpus, general proper nouns that are not specific to the Ukraine War (e.g. US, UK, EU, Facebook, Guterres, Biden), as well as the names that refer to different individuals or entities (e.g. Urey, Healy, Samara), are excluded in this analysis. There are 27 proper nouns that do not appear in any of the Leipzig corpora. This 54 (Back to Contents) Comparative studies: Ukraine War corpus VS Leipzig corpora leaves us with 23 proper nouns of interest sorted under four main categories: people, locations, terms and concepts, and organizations. The list of proper nouns are in Table 13 below. Table 13: Proper nouns of interest Category Sub-category Description Proper Nouns (Leipzig) People Russia Gov./Military Officials Putin, Medvedev Activists and Advocates Navalny Ukraine Gov./Military Officials Zelensky, Yushchenko, Yanukovych, Serhiy, Nikulin Others International Victims Shapps Locations Russia Russian Locations Russia, Moscow Russian Nationality Russia Ukraine Ukrainian Locations Ukraine, Kyiv, Donetsk, Donbas, Luhansk Ukrainian Nationality Ukrainian Disputed Areas Crimea Terms & Concepts UAV, PoWs, Neptune Organizations Gazprom 5.1.2.3.1 Proper noun categories through time The categorical shift of the proper noun is investigated in terms of the proportions, which are the normalized occurrences presented as words per million. This is calculated by dividing raw frequencies with total occurrences of proper nouns in the corpus and multiplying with 1 million. The occurrence weights in each category and year are also reported as a percentage. Normalized Proper Noun Occurrences =Raw Frequencies Total Passive Subjects ∗ 1,000,000 Table 55 in the Appendix shows the proportion of the proper nouns related to the Ukraine War in the Leipzig corpora. By categories, Locations occupy 60.68% followed by People at 37.57%. Terms and Concepts, as well as Organizations make up for only 1.33% and 0.42%, respectively, of the passive proper noun subjects in the Leipzig corpora. By sub-categories, proper nouns related to Russia account for more than half of both People and Locations. 80.36% of the People category has been found to be Russian Officials, as well as Russian Activists and Advocates, and 62.57% of the Location category is dominated by Russian Locations and Nationality.Ukrainian proper nouns, on the other hand, share only 9.56% in the People category and 28.96% in the Location category. The rest of the People category belongs to International Victims at 10.07%, and the rest of the Location category goes to Disputed Areas at 8.46%. Analyzing the proportion of words within sub-categories shows that the main Ukrainian figures are Zelensky (the sixth president of Ukraine since 2019 at 58.59%) and Yanukovych (the fourth president of Ukraine from (Back to Contents) 55 Comparative studies: Ukraine War corpus VS Leipzig corpora 2010 to 2014 at 28.05%). For Russia, the most frequently and consistently mentioned individuals are Putin (93.99%). In terms of locations,Ukraine (74.71%), Kyiv (11.17%), and Donetsk (10.67%), are among the top names for Ukraine. For Russia, the list includes Russia (78.15%) and Moscow (21.29%). From the normalized occurrences, Crimea is found in the corpora around 50% less frequently than Ukraine (222.07 and 481.54 hits, respectively). The comparison of the proper noun categories identified in the Ukraine War corpus and the Leipzig corpora is illustrated in Figure 6 and Figure 7 in the next page. Figure 12: Proper noun categories in the Ukraine war corpus Figure 7: Proper noun categories in the Leipzig corpora Compared to the categorical proportion in the Ukraine War corpus, a similar trend is observed with some minor different details. Location category still dominates; however, the weight in the 56 (Back to Contents) The examination of passive voice in the Leipzig corpora Leipzig corpora is more prominent at 62.26% compared to 50.98% in the Ukraine War corpus. The more weight of the Location proper nouns results from an increase in all sub-categories; Russian Locations from 30.72% to 38.30%, Ukrainian Locations from 16.34% to 18.75%, and Disputed Area (Crimea) from 1.31% to 5.21%. People category in the Leipzig corpora shrinks down by 6.77% (from 41.83% to 35.06%). The percentage of sub-categories remains the same where Russian People Proper Nouns occupy the largest portion with an increase from 28.76% in the Ukraine War corpus to 30.61% followed by Ukrainian People and Other Nationalities decreasing from 5.88% to 3.64% and from 1.96% to 0.81%, respectively. The Names of People found in the Ukraine War corpus are more diverse with the British and the US sub-categories being present accounting for 3.92% and 1.31%, respectively. Terms and Concepts, as well as Organizations, are mentioned sparinglyat 2.26% and 0.42%, respectively, decreasing from 3.92% and 3.27% in the Ukraine War corpus. In summary, when it comes to the Ukraine War situation, news in the Leipzig corpora discusses Locations proportionally more often than People with the proper nouns related to Russia more than Ukraine in both categories. In addition, the proper nouns in the Ukraine War corpus show more variety when compared to a general news corpora like the Leipzig ones. The comparison in details is illustrated in Figure 23 in the Appendix. Moving forward Up until this point, we extracted passive subject-verb pairs from the Ukraine War corpus and categorize subjects and verbs to build the framework for later analyses. In the following sections, we are about to dive deeper to examine PASSIVE VOICE in the Leipzig corpora in terms of the trend and figure out how popular the topic related to Russia-Ukraine conflict is through the years. We will also analyze how RUSSIA and UKRAINE are represented as ACTOR and PATIENT in passive clauses in the Ukraine War corpus. We then compare the findings from the Western media with the Russian news sources. 5.2 The examination of passive voice in the Leipzig corpora This section aims to explore the trends and phenomena related to passive voice in Leipzig Corpora, namely the common pairs compared with the Ukraine War corpus, active VS passives, short VS long passives, beVS getpassives, and the popularity of Ukraine War topic. (Back to Contents) 57 The examination of passive voice in the Leipzig corpora Corpus # Sentence # Word # Passive Pair # By-agent % Long Passive % Short Passive Leipzig_2018 839,051 17,951,766 126,646 16,489 13.02 86.98 Leipzig_2019 845,091 18,128,565 125,510 16,366 13.04 86.96 Leipzig_2020 846,917 18,342,325 132,617 15,876 11.97 88.03 Leipzig_2023 854,997 18,498,430 125,547 16,297 12.98 87.02 Average 823,249 17,914,504 125,790 16,159 12.87 87.13 According to the calculation, BY-AGENT passives account for 12.87% and 87.13% for a short form of passives, which is in accordance with the findings of more than 70% of short passives by Jespersen (1933) and more than 80% by Svartvik (1966). Figure 11: Percentage of short passive in Leipzig corpora Figure 11 shows an increase in the usage of SHORT PASSIVE in Leipzig news corpora. Even when the unusually high occurrences of SHORT PASSIVE in the year 2020 (88.03%) are substituted with an average percentage of 86.99%, a rising trend still shows. 5.2.2.3 BeVS Get-passive BE and GET are extracted first by identifying passive voice construction with the dependency tag nsubjpass. Then, auxiliary verbs are captured through the dependency tag auxpass, which stands for auxiliary passive. Auxiliary verbs BE and GET are extracted separately; if aux == "be", the subject-verb pair is put in the BE-PASSIVE list, and if aux == "get", the pairs are appended to the GET-PASSIVE list. The dependency parse trees of BE and GET are illustrated below. 64 (Back to Contents) The examination of passive voice in the Leipzig corpora Figure 12: The dependency parse trees of be passive Figure 13: The dependency parse trees of get passive After the BE and GET are extracted, a number of BE and GET pairs are counted. The percentage of BE and GET-PASSIVE is calculated based on a number of pairs. The results are reported in the table below. Table 20: Percentage of beand getpassives pairs in the Leipzig corpora Corpus #Sent. #Word #Be Pass. #Get Pass. %Be Pass. %Get Pass. Leipzig_2014 756,346 16,859,763 118,696 962 99.20 0.80 Leipzig_2015 765,565 17,199,696 123,720 867 99.30 0.70 Leipzig_2016 841,467 18,425,414 121,037 1,038 99.15 0.85 Leipzig_2017 836,561 17,910,026 123,322 1,180 99.05 0.95 Leipzig_2018 839,051 17,951,801 123,499 1,192 99.04 0.96 Leipzig_2019 845,091 18,128,872 122,446 1,159 99.06 0.94 Leipzig_2020 846,917 18,342,507 129,541 1,209 99.08 0.92 Leipzig_2023 854,997 18,498,586 122,675 1,134 99.08 0.92 Avg. (Lzg) 823,249 17,914,583 123,117 1,093 99.12 0.88 The result table shows that GET in the Leipzig corpora account for only 0.88% on average. Figure 14 below shows the percentage of GET-PASSIVE through time in the Leipzig corpora. The trendline indicates a gradual increase of GET-PASSIVE occurrences in news media, which is in accordance with the claim of Di Ferrante (2023).Di Ferrante conducts a thorough research and notes that several studies have recorded an increase of the use of the GET-PASSIVE constructions (Back to Contents) 65 The examination of passive voice in the Leipzig corpora between the end of the 20th century and the first two decades of the 21st century (See Biber & Gray 2016;Givón & Yang 1994;Mair & Leech 2006). Figure 14: Percentage of get-passives in the Leipzig corpora According to different types of connotations that GET PASSIVE convey proposed by Hatcher (1949) and Lakoff (1971), there are only a couple of scenarios where the connotations can be found in this study. These scenarios are constrained by genre and context of the datasets. Because this analysis focuses on news reports, a certain level of OBJECTIVITY has to be maintained as also mentioned by Lakoff (1971) in Section 2.3.3.2. This puts a limitation on the EMOTIONAL INVOLVEMENT connotation. In other words, the “feelings” of a speaker or a writer cannot be assumed from the news reports. Moreover, in the context of news reports, a speaker or a writer does not talk about themselves, so it is unlikely to reflect if something is good or bad for them. This crosses out the EVALUATIVE ATTITUDE connotation. This leaves us with only SYMPATHY and AGENCY connotations. Take this news report from the 2014 Leipzig corpus for example. (51) a. Where Russia gets hit hardest among the sanctions announced Tuesday, the capital restrictions are expected to have the most damaging effect on Russia. b. Though the damages awarded fell far short of the amount sought, the ruling was one more blow to Russia which has just been hit with a second, tougher round of U[...] Implementing Chappell’s (1984) framework on the connotations of GET PASSIVE, in Example 51a, a sense of sympathy for Russia’s experience of adverse effects or hardships due to the sanctions is very clear with the use of GET PASSIVE. The use of SUPERLATIVE comparison the hardest and the most damaging effect accentuates the INFERENCE to the suffers. In Example 51b, on the other hand, BE PASSIVE makes the tone sounds declarative, presenting the information 66 (Back to Contents) The examination of passive voice in the Leipzig corpora straightforwardly that the sanctions are “another challenge” for Russia. There is no heightened emotional or emphatic tone seen in Example 51a. Regarding the AGENCY,GET PASSIVE in Example 51a portrays Russia as the subject who possesses some degree of AGENCY in the situation, suggesting although Russia is negatively impacted by the sanctions, Russia is not an “innocent victim” and may have an ability to handle the circumstances leading to the outcome. Conversely, BE PASSIVE in Example 51b depicts Russia as a purely passive recipient of the sanctions. This connotation is strengthened with the reference to the authority the ruling, meaning a decision made by a court or legal authority. In addition, the amount sought refers to the damages requested by the plaintiff in the legal case, highlighting the passive position of Russia in receiving the consequences of the ruling. The phrase one more blow conveys a cumulative sense of ADVERSITY, further reinforcing Russia’s passive position as a target of “repeated” negative actions. Another aspect related to GET PASSIVE worth examining in the future study is inspired by the claim of Zúñiga & Kittilä (2019: 100) that GET PASSIVE occurs more frequently with NEGATIVE verbs that imply negative effects on the GRAMMATICAL SUBJECT. This claim is linked to SYMPATHY CONNOTATION in Section 2.3.3.3 where a speaker uses GET PASSIVE to make an INFERENCE of a NEGATIVE nature about the SUBJECT. Apart from the declining trend of PASSIVE VOICE usage, the rising trend for SHORT PASSIVE and GET PASSIVE against their counterparts, another aspect worth examining is how popular the topic related to the Ukraine War is in Western news media. 5.2.3 Ukraine War is sometimes a big deal: Topics in news reports To investigate how commonly the Ukraine War has been presented in the news media through time, a topic modeling method is employed to examine popular topics in each year of the Leipzig corpora. Text preprocessing was conducted using spaCys transformer model en_core_web_trf. The pretrained language model BERT is chosen due to its capabilities in understanding context and semantics compared to traditional models like Gensims Latent Dirichlet Allocation (LDA) leading to more coherent and meaningful topics. To see all topics of each years corpus, one corpus is processed one at a time. The datasets are sub-corpora that contain only passive voice sentences. The statistics of the corpora are shown in Table 21. Table 21: Statistics of passive voice sentence sub-corpora Corpus #Sentence #Word Word/Sent. passive_text_leipzig_2014.txt 141,353 3,219,523 22.78 passive_text_leipzig_2015.txt 145,011 3,350,747 23.11 passive_text_leipzig_2016.txt 147,064 3,354,242 22.81 Continued on next page (Back to Contents) 67 The examination of passive voice in the Leipzig corpora Corpus #Sentence #Word Word/Sent. passive_text_leipzig_2017.txt 150,622 3,471,914 23.05 passive_text_leipzig_2018.txt 151,574 3,503,306 23.11 passive_text_leipzig_2019.txt 149,241 3,454,805 23.15 passive_text_leipzig_2020.txt 156,139 3,649,509 23.37 passive_text_leipzig_2023.txt 150,128 3,505,583 23.35 Average 148,892 3,438,704 23.09 Only the topics with the significance scores greater than 500 are analyzed. If the topic related to the Ukraine War receives less than 500 significance scores, it is manually appended at the end of the list. This also applies to 8 recurring topics (in the graph below). The significance scores are normalized by dividing with a total number of words in a corpus and multiplying with 1 million. Normalized Significance Score =Original Score Corpus Size ∗ 1,000,000 A topic of each group is derived using ChatGPT-4o to summarize member words in that specific group. Table 57 in the Appendix illustrate the transformation of the top 4 topics in the score report of the 2015 Leipzig corpus (3,350,747 words). The topics from each year with the significance scores are demonstrated in Table 58 and Table 59 in the Appendix. The rankings of the Russia-Ukraine conflict topic are in the squared brackets. The full summary tables with member words can be found in Appendix 4. Together with the topic of Russia-Ukraine Conflict, there are 8 recurring topics across the years: Education, Gun Violence, Firefighting, Music, Film Industry, Parenting, Weather, and IsraelPalestine Conflict.Figure 15 below shows the significance score of each topic in the Leipzig corpora (years 2014-2020 and 2023). 68 (Back to Contents) The examination of passive voice in the Leipzig corpora Figure 15: Popular topics in the Leipzig corpora Topic modeling method reveals a similar trend with the one of the proper noun categories; topics related to the Russia-Ukraine conflict gained the highest attention in 2014 when the war started and in 2023 when the invasion began. Actually, the significance score of the year 2018 was not so different from in 2023. The years in between showed lower engagement of this topic in the news with a surge in 2017 and 2018. Figure 15 not only shows a similar evolving representation between the proper nouns and the topics related to the Ukraine War but also demonstrates the rankings of the Russia-Ukraine War topic compared to the other recurring ones presented in passive voice in the news. The rankings indicate that the dominant topics are related to general themes, namely Education,Music, Parenting, and Fire and Firefighting, which can be identified in the top10 in almost every year, while themes related to war and violence, such as Israel-Palestine Conflict,Gun Violence, and Russia-Ukraine Conflict, do not receive as much importance. The only exception is in the years 2014 and 2015 where the Russia-Ukraine War topic is in a high ranking in the second and fourth place, respectively, as the war began. The topic modelling method offers an overview of the thematic representations across time, where the popularity trend of the Ukraine War topic is revealed. This utilization represents a different type of application compared to what Ylä-Anttila et al. (2022) refers to as the validation of the outputs, which ensures that “the word clusters actually represent what we think they do”. Moving forward, a closer look is to be taken with the analysis BY-AGENT in a word level revealing how AGENCY and RESPONSIBILITY are assigned differently in the context of the Russia-Ukraine War. (Back to Contents) 69 Conceal and highlight: By-agent and modifier analysis 5.3 Conceal and highlight: By-agent and modifier analysis 5.3.1 By-agent semantic categories The categorization of BY-AGENTS is conducted on the Ukraine War corpus based on six semantic roles: agentive, instrumental, undergoer, experiencer, recipient, and beneficiary. There are 165 passive subject-verb pairs with BY-AGENTS. The sampled 20 pairs are as follow: Table 22: The sampled 20 pairs of by-agent phrases with the corresponding subject-verb pairs Subject_Verb Pairs By-agent Phrases attack_captured by_Baykar_Bayraktar_TB2_drone attack_claimed by_hacker_cooperative attacks_repelled by_selfless_actions_Russian_military_personnel attacks_staged by_Russia building_hit by_apparent_rocket_strike building_struck by_missile building_struck by_drone_Russia claim_corroborated by_Ukrainian_parliament_Operational_Command claim_used by_President_Vladimir_V._Putin claims_dismissed by_international_community evacuation_thwarted by_Russian_military medic_killed by_second_missile Petrova_escorted by_police story_haunted by_Meghan_Markle advance_slowed by_resistance advances_limited by_Ukrainian_defences areas_controlled by_installed_leaders areas_controlled by_backed_separatists base_destroyed by_Ukrainian_artillery base_plagued by_corruption The examination identifies only two semantic roles: AGENTIVE and INSTRUMENTAL. During the categorization process, there are six sentences that contain the thematic role CIRCUMSTANCE in by-phrase. The examples are listed below. 1. The difficulty is accentuated by geography. 2. Russia’s present-day war of aggression is refashioned by propaganda... 3. Symbols are sized by the minimum number of days with reports of such attacks. The by-phrases in these sentences do not fall into any of the six semantic roles. Instead, they describe the context or the condition under which the action occurs. These by-phrases are, therefore, excluded from the analysis. The categorization results are in Table 23. The frequencies of occurrences are placed in the parentheses. 70 (Back to Contents) Conceal and highlight: By-agent and modifier analysis Table 23: Semantic roles of by-agent phrases with frequencies in the parentheses Semantic Roles Words Total % Agentive force (10), russia (8), ukraine (6), drone (6), missile (5), separatist (4), weapon (4), weaponry (3), government (3), president (3), putin (3), resistance (3), actor (2), shelling (2), shell (2), attack (2), committee (2), district (2), fighting (2), leader (2), media (2), military (2), parliament (2), people (2), rebel (2), strike (2), bomb (2), ukrainians (2), agreement, ally, authority, battle, bombardment, centre, cnn, comedian, command, commission, community, conflict, congress, corporation, corruption, country, cut, defence, director, dude, enemy, factor, federation, firm, folk, friend, frontline, gazprom, geography, guard, hacker, inconsistency, institute, intelligence, journal, loss, luhansk, markle, member, nationalist, nazis, object, organization, paratrooper, personnel, police, pravda, press, public, representative, service, shoigu, side, siren, soldier, sound, spokesman, traffic, troop, victory, warship, washington, west, whistleblower, artillery, attack, dioxin, duel, fire, projectile 158 99.37 Instrumental stretcher 1 0.63 Total 159 100.00 Almost all of the by-phrases are categorized as agentive (99.38%) and only 0.62% as instrumental. It would be more insightful to further categorize the agentive by-phrases. The categories are adapted from subject categories in the Leipzig corpora, and the semantic map Figure 24 is utilized to aid the categorization. The categories of BY-AGENT in the Ukraine War corpus is shown in the Table 24 below. Table 24: Categories of by-agent in the Ukraine War corpus Category Words (f) % Weapon drone (6), missile (5), weapon (4), weaponry (3), bomb (2), shelling (2), shell (2), warship, duel, dioxin, artillery 28 17.72 Military and Authority force (10), military (2), authority, command, frontline, guard, intelligence, defence, paratrooper, personnel, police, service, soldier, troop 24 15.19 Non-military Conflicting Parties separatist (4), resistance (3), actor (2), rebel (2), nationalist, hacker, enemy, ally, Nazis, whistleblower, side 18 11.39 Country and City Russia (8), Ukraine (6), Luhansk, Washington 16 10.13 Law and Governance government (3), committee (2), parliament (2), agreement, federation, congress, representative, spokesman, corruption, corporation 14 8.86 Death and Danger siren, fighting (2), attack (2), conflict, loss, bombardment, battle, projectile, fire, strike (2) 13 8.23 Important Figures Putin (3), president (3), leader (2), director, Gazprom, Markle, Pravda, Shoigu 13 8.23 Continued on next page (Back to Contents) 71 Conceal and highlight: By-agent and modifier analysis Category Words Frequency % Civilian people (2), Ukrainians (2), dude, folk, community, friend, member, public 10 6.33 Location centre, firm, country, institute, district (2), west 74.43 Media, Entertainment, Technology media (2), CNN, comedian, journal, press 6 3.80 Neutral Entities object, factor, geography, sound, victory 5 3.16 Monetary and Business cut, commission 2 1.27 Commute traffic 1 0.63 Nominalization inconsistency 1 0.63 Total 158 100.00 After categorization process, it has been found that BY-AGENTS in the Ukraine War corpus are occupied by words related to weapon (17.72%), military and authority (15.19%), non-military conflicting parties (11.39), and country and city (10.13%), closely followed by law and governance (8.86%), death and danger (8.83%), important figures (8.23%), and civilian (6.33%). Location, as well as media, entertainment and technology and neutral entities, are minimally identified at 4.43%, 3.80%, and 3.16%, respectively. Rarely found at only 0.63% are BY-AGENTS related to commute and nominalized words. The results from categorizing BY-AGENTS indicate that AGENCY and RESPONSIBILITY are assigned to weapons as inanimate entities slightly more than human entities, namely military and authority, as well as non-military parties. In the context of war and conflict, it may be as necessary to specify who causes the action as what kind of weapons does. Taking a closer look at the most frequently used BY-AGENTS from human entities (force, military, separatist) and non-human entities (drone, missile, weapon) and what kind of actions they perform, a couple of critical observations can be made based on Table 25 and Table 26 below. Table 25: Human agents in by-agent phrases Subject_Verb Pairs Human Agents bridges_destroyed by_Ukrainian_forces, by_Russian_troops conflict_marked by_artillery_duels, by_special_forces, by_trench Mykolaiv_shelled by_Russian_forces number_reclaimed by_Ukrainian_forces town_occupied by_Russian_forces Ukrainians_killed by_Russian_forces Urey_captured by_Russian_forces Center_run by_Special_Forces citizen_detained by_pro_-_Russian_separatists_forces corridor_thwarted by_Russian_forces fighters_held by_Ukrainian_forces plant_seized by_Russian_forces Continued on next page 72 (Back to Contents) Conceal and highlight: By-agent and modifier analysis attacks_repelled by_selfless_actions, by_Russian_military_personnel evacuation_thwarted by_Russian_military axis_deployed by_Western_Military_District, by_eastern front_opened by_Southern_Military_District supply_held by_Russian_military areas_controlled by_backed_separatists region_run by_backed_separatists, by_Luhansk_Republic citizen_detained by_pro_-_Russian_separatists_forces territories_controlled by_backed_separatists Table 26: Non-human agents in by-agent phrases Subject_Verb Pairs Non-human Agents attack_captured by_Baykar_Bayraktar_TB2_drone building_struck by_drone, by_Russia craft_destroyed by_Ukrainian_drone refinery_attacked by_drone strike_attacked by_Russian_drones tanker_damaged by_maritime_drone building_struck by_missile medic_killed by_second_missile Explosions_caused by_long-range_missiles_by_Ukraine, by_United_Kingdom people_killed by_Russian_missile_attack, by_southern_Ukrainian_city_Odesa people_killed by_missile enemy_struck by_aviation_missile deaths_caused by_explosive_weapons_by_wide_area_effects tanks_demolished by_small_Ukrainian_defensive_units, by_weapons_such_as_Stingers targets_struck by_unconfirmed_weaponry University_hit by_weapons Table 25 and Table 26 reflect how agency and responsibility are delegated differently between HUMAN and NON-HUMAN AGENTS. The verbs that occur as actions caused by NON-HUMAN AGENTS are mostly VIOLENT verbs (destroy, attack, damage, strike, kill, demolish, hit) at 81.25% (13/16 counts) while the verbs used with HUMAN AGENTS are mainly RESTRICTIVE verbs (occupy, capture, detain, thwart, hold, seize, control) at 52.38% (11/21 counts), and VIOLENT verbs associated with HUMAN AGENTS are only found at 14.29% (3/21 counts). This implies that news reports tend to avoid explicitly portraying HUMAN AGENTS to perform VIOLENT actions and assign the VIOLENT actions to NON-HUMAN AGENTS instead. At the same time, LESS VIOLENT actions, i.e. RESTRICTIVE actions, are preferably represented to be caused by human actors. This finding adds up to the emotionally demoting effect of passive voice when BY-AGENTS are present. The strategy is implemented through the assignment of VIOLENT verbs to NON-HUMAN actors and LESS-VIOLENT verbs to HUMAN actors. The next section further investigates how (Back to Contents) 73 The perspectives from another side: Russian news sources Table 36: Topic keywords and scores of the Russian news sources # Topic_Arctic Score Topic_TASS Score Topic_TMCM Score 1Icebreakers 368.03 Russian Economy 628.18 Crime and Law Enforcement 952.88 2Ports and Routes 245.36 Arts and Culture 377.82 Middle East Conflict 406.30 3Budget and Finance 225.73 Climate and Wildfires 332.30 Military and Defense 353.10 4Polar Wildlife 201.19 Cuisine 304.98 Economic Development 348.26 5Legal Framework 196.29 Conflict and Casualties 218.50 Ukraine Conflict 237.01 6Education 196.29 Flooding and Rivers 218.50 Natural Disasters 232.17 7Government and Development 186.47 Arrests and Trials 213.94 International Treaties 227.34 8Climate Change 186.47 Environmental Issues 204.84 Sports and Doping 222.50 9Energy Resources 186.47 Political Appointments 204.84 Space Exploration 183.80 10 Economic Development 181.56 Chechnya and Kadyrov 172.98 Energy Sector 183.80 11 Arctic Council 181.56 Drone Attacks 145.66 Ceasefire Agreements 174.13 12 Future Expectations 181.56 Family and Custody 145.66 Diplomatic Meetings 169.29 13 Arctic Geography 171.75 Georgia and Politics 145.66 Economic Forums 164.46 14 Forums and Events 132.49 Ukraine Conflict 122.90 Presidential Delegations 140.27 15 Indigenous Peoples 117.77 Linguistics 122.90 Soccer Tournaments 130.60 *TMCM = The Moscow Times Table 36 shows that the topic related to the Ukraine conflict is ranked fourteenth in TASS corpus and fifth in the Moscow Times corpus, and there is no discussion about Ukraine in the Arctic corpus. The full tables with keywords and scores can be found in Table 51 - 53 in the Appendix. The topics of each corpus reflect clearer characteristics of the Russian news media. Arctic News Media primarily focuses on Arctic-specific issues including environmental,economic, and infrastructural topics, with significant attention to polar wildlife and climate change. TASS provides comprehensive coverage of economic issues, cultural events, and climate-related topics, with additional focus on legal issues and political developments. The Moscow Times focuses heavily on crime and law enforcement, with significant attention to international conflicts,military issues, and economic development. These results confirm and enhance the findings from the previous section. With the highest score of the Ukraine conflict topic and the topics related to international conflicts (Middle East and Ukraine), as well as military and defense, it is clear that The Moscow Times (TMCM) has the most international outward-looking outlook. For this reason The Moscow Times corpus is chosen in the by-agent and modifier analysis to examine how Russia and Ukraine are represented. 80 (Back to Contents) The perspectives from another side: Russian news sources 5.4.2 By-agent and modifier analysis The same BY-AGENT AND MODIFIER ANALYSIS is conducted to analyze the representations of RUSSIA and UKRAINE in the Russian news corpora (The Moscow Times corpus). Four proper nouns (Russia, Russian, Ukraine, Ukrainian) are investigated in pairs and in three positions: MODIFIER OF SUBJECT,SUBJECT, and BY-AGENT. Only the pairs with p<0.05 are analyzed. Since Ukraine and the occurrences of Ukrainian are low based on the p-value filter (only 1 as aMODIFIER and none for the other two positions), the filter is removed to get adequate data for the analysis. The results are as follow: 5.4.2.1 Russia and Russian as modifier Table 37: Russia and Russian as modifier Modifier Subject_Verb Agent Russian Il-20_shot by_air_defense Russian peacekeepers_deployed Russian peacekeepers_deployed Russian sailors_suspected Russian sailors_suspected single_Russian_military serviceman_left Russian servicemen_activated - RUSSIAN + 4 NEUTRAL VERBS(deploy, leave, activate) [57.14%] + BY RUSSIA/RUSSIAN X - RUSSIAN + 1 VIOLENT VERB (shoot) [14.29%] + BY RUSSIA/RUSSIAN X - RUSSIAN + 2 NEGATIVE VERB (suspect) [28.57%] + BY RUSSIA/RUSSIAN X 5.4.2.2 Russia and Russian as subject Table 38: Russia and Russian as subject Modifier Subject_Verb Agent Russia_represented by_Deputy_Foreign_Minister_Sergey_Ryabkov Russia_represented by_Russian_Prime_Minister_Dmitry_Medvedev Russia_represented by_Russian_Prime_Minister_Dmitry_Medvedev Russia_guided by_United_documents_Sustainable_Development_them hosts Russia_set Russia_stuck Russia_stuck Russia_punished Russia_scheduled Russia_blamed Russia_affected Continued on next page (Back to Contents) 81 The perspectives from another side: Russian news sources Modifier Subject_Verb Agent Russia_set Russia_accused Russian_sent Russian_set Russian_beaten Russians_suspected Russians_suspected - RUSSIA + 7 NEGATIVE VERBS (punish, blame, affect, accuse, beat, suspect) [38.89%] + BY RUSSIA/RUSSIAN X - RUSSIA + 2 RESTRICTIVE VERB (stick) [11.11%] + BY RUSSIA/RUSSIAN X - RUSSIA + 5 NEUTRAL VERBS (set, send, schedule) [27.78%] + BY RUSSIA/RUSSIAN X - RUSSIA + 4 POSITIVE VERBS (represent, guide) [22.22%] + BY RUSSIA/RUSSIAN X 5.4.2.3 Russia and Russian as agent Table 39: Russia and Russian as agent Modifier Subject_Verb Agent agreement_signed by_General_Serbian_Railways_Infrastructure_Miroljub agreement_signed by_Minister_Construction_Zorana_Director_Russian_Railways_Oleg roadmap criteria_fulfilled by_Russian_side event organized by_Bank_Russia event organized by_Bank_Russia Princess accompanied by_representatives_Saransk_eparchy_Russian_Orthodox_Church Russia represented by_Russian_Prime_Minister_Dmitry_Medvedev Russia represented by_Russian_Prime_Minister_Dmitry_Medvedev -SOMETHING + 5 POSITIVE VERBS (represent, fulfill, sign) [62.5%] + BY RUSSIA -SOMETHING + 3 NEUTRAL VERBS (accompany, organize) [37.5%] + BY RUSSIA 5.4.2.4 Ukraine and Ukrainian as modifier Table 40: Ukraine and Ukrainian as modifier Modifier Subject_Verb Agent Ukrainian citizens_obliged Ukrainian_armed forces_put by_decision_chief_General_Staff new_Ukrainian government_formed by_members_former_president Ukrainian_education law_amended Ukrainian_crew members_arrested Ukrainian_crew members_arrested Ukrainian sailors_detained Continued on next page 82 (Back to Contents) The perspectives from another side: Russian news sources Modifier Subject_Verb Agent Ukrainian servicemen_wounded Ukrainian ships_detained Ukrainian side_geared - UKRAINIAN + 1 HARMFUL VERBS (wound) [10%] - UKRAINIAN + 5 NEUTRAL VERBS (oblige, put, form, amend, gear) [50%] - UKRAINIAN + 4 RESTRICTIVE VERBS (detain, arrest) [40%] 5.4.2.5 Ukraine and Ukrainian as subject Table 41: Ukraine and Ukrainian as subject Modifier Subject_Verb Agent Ukraine_crowded 5.4.2.6 Ukraine and Ukrainian as agent Table 42: Ukraine and Ukrainian as agent Modifier Subject_Verb Agent other broadcasters_placed by_Council_Ukraine inauguration ceremony_attended by_former_Ukrainian_Presidents document_signed by_Verkhovna_Rada_speaker_Ukraine document_signed by_Verkhovna_Rada_speaker_Ukraine total_fired by_Ukrainian_troops total_fired by_Ukrainian_troops -SOMETHING + 2 VIOLENT VERB (fire) [33.33%] + BY UKRAINE -SOMETHING + 4 NEUTRAL VERBS (sign, attend, place) [66.67%] + BY UKRAINE 5.4.2.7 Interpretation of the results (Russian news corpora) Based on the SYNTACTIC-SEMANTIC SCHEMAS, RUSSIA and RUSSIAN behave differently in each position, taking on HARMFUL and NEGATIVE verbs at the MODIFIER position (42.86%), NEGATIVE and RESTRICTIVE verbs as a SUBJECT (50%), and only POSITIVE and NEUTRAL as AGENT (100%) while UKRAINE and UKRAINIAN are found with RESTRICTIVE and HARMFUL verbs as a MODIFIER (50%) and VIOLENT verbs as AGENT (33.33%). Both UKRAINE and RUSSIA are often used with NEGATIVE verbs including HARMFUL and RESTRICTIVE verbs as a MODIFIER; for example, Russian_sailors_suspected and Ukrainian_crew_members_arrested. As an AGENT, on the other hand, RUSSIA and UKRAINE are represented differently; RUSSIA are associated with only POSITIVE and NEUTRAL verbs while UKRAINE is found with VIOLENT verbs. (Back to Contents) 83 The perspectives from another side: Russian news sources 5.4.2.8 Comparison with the analysis from the Ukraine War corpus In summary, by comparing the representations of RUSSIA and UKRAINE in the Ukraine War corpus found in Section 5.3.2.7 and the Russian news corpora using the BY-AGENT AND MODIFIER ANALYSIS method, the following findings are identified. RUSSIA is portrayed more negatively often in a context of AGGRESSION,LIMITATION, or NEGATIVITY in the Ukraine War Corpus compared to how it is depicted in Russian news sources, especially in the AGENT position that portrays RUSSIA as acting in a positive or neutral manner. UKRAINE is depicted more positively in the Ukraine War Corpus, especially as an AGENT, compared to the more adversarial representation in Russian news sources. The frequency of VIOLENT verbs associated with UKRAINE in both corpora indicates a focus on conflict-related actions, while Russia’s association with POSITIVE verbs in the Russian news sources contrasts with its more NEGATIVE portrayal in the Ukraine War Corpus. 84 (Back to Contents) 6 Conclusion The extensive analysis presented in this study provides a comprehensive view of passive subjectverb pairs, proper nouns, and passive voice in general. The examination is conducted in the Ukraine War corpus, representing a specific corpus, Leipzig corpora, representing a general corpus, and Russian news corpora, representing news reports from the opposition stand point. A combination of NLP and Corpus Linguistics methods helps to reveal agency and responsibility, representations, as well as trends over time. The main tools are spaCy parser used to extract passive pairs, as well as t-score and p-value, used as the measure of collocational strength and the filter of the significant pairs. Semantic map aids with the categorization of subjects and verbs, and topic modeling shows the popularity of topic related to the Ukraine War in news across the years. The analyses start from building a framework based on the Ukraine War corpus including subject and verb categories, important figures, and proper noun representation. These findings are then compared with more general news reports like the Leipzig corpora. Leipzig corpora, with the datasets expanding for almost a decade, allow for diachronic analyses, namely active and passive voice, short and long passives, as well as beand getpassives. By-agent and modifier analysis further reveals how Russia and Ukraine are portrayed. The study would not have been completed without taking into consideration the Russian viewpoint. Using the Russian news sources, topic modeling and by-agent analysis are conducted. Harnessing the NLP and Corpus Linguistics methods applied on three different corpora, the results reveal how the agency and responsibility are delegated, how Russia and Ukraine are represented, and in a broader picture, how the usage of passive voice evolves over time. Subject and verb categorization in the Ukraine War corpus in Section 5.1.1.3.3 provides a framework for later analyses. The comparison of passive subject-verb pairs between the Ukraine War corpus and the 2014 and the 2023 Leipzig corpora in Section 5.2.1.3 shows an interesting finding. Although the Leipzig corpora are non-specific news corpora, they shares similar passive pairs with the Ukraine War corpus, emphasizing civilian and military subjects, as well as violent and restrictive verbs. Proper noun representations of Russiaand Ukraine-related entities in the Ukraine War corpus differ significantly (see Section 5.1.2). Russian entities are depicted using the euphemism signaling mechanistic dehumanization, reducing emotional impact and individual recognition. In 85 Conclusion contrast, Ukrainians are often portrayed as victims through the use of violent verbs. Additionally, activists, advocates, poets, and artists appear more frequently under Russian nationalities, emphasizing the malevolent image of Russia who suppresses the dissent and creative expressions of their own citizens. This malicious portrayal of Russia also extends to the foreign citizens since international victims are identified. The analysis of proper nouns over time in Section 5.1.2.3 reveals that the usage of proper nouns related to the conflict follows a U-shaped pattern, peaking in 2014 and 2023, corresponding to significant events like the annexation of Crimea and the 2023 invasion. Topic modeling in Section 5.2.3 indicates similar trends, with high attention to the Russia-Ukraine conflict in 2014 and 2023, interspersed with fluctuations reflecting ongoing diplomatic and cyber-attack events. The diachronic analysis of passive voice in Section 5.2.2 also indicates that the usage of short passives and get passives increases over time, but the usage of passive voice in general gradually decreases. The by-agent and modifier analysis in Section 5.3 indicates that agency and responsibility are more frequently assigned to non-human entities like weapons, which are associated with violent actions. Human agents are more often linked with restrictive actions, suggesting a narrative strategy that distances human actors from direct violence. Furthermore, Russia is depicted negatively in the Ukraine War corpus, especially in agent roles, whereas in Russian news sources in Section 5.4.2.7, it is associated with positive or neutral actions. Ukraine, conversely, is portrayed more positively as an agent in the Ukraine War corpus but is associated with conflict-related actions in Russian news sources. 86 (Back to Contents) 7 Discussions This project provides a nuanced analysis of how passive voice constructions in news media shape the portrayal of Russia and Ukraine, highlighting significant differences between Western and Russian media. It also underscores the importance of contextual and comprehensive analysis when interpreting passive voice usage and news media. This study also introduces further considerations regarding the biases inherent in media sources, opens up for the implications for future research, and raises critical limitations related to computational tools used. A critical insight of this study is that passive voice constructions alone may not fully capture how responsibility and agency are portrayed. Both victim blaming (Bohner 2001) or culprit hiding (Katz 2006) need to be understood within the broader context of the entire news report. While TV news once dictated the pace of information consumption (Homan 1989), online news now allows readers to control their reading time. This shift allows for the examination of an entire article and their broader context. The study shows that passive voice serves various purposes beyond obscuring the actor or minimizing wrongdoing. While it can distance readers from the actor, it also serves stylistic or narrative functions. This complexity necessitates a nuanced perspective towards passive constructions within the context of news reporting and in general. The U-shaped pattern in proper noun representation and fluctuations in Leipzig corpora during significant events reflect both the importance of these events and the media’s tendency to perpetuate situations to attract attention from the audience. The media’s role in amplifying the significance of events through continuous reporting and reprinting highlights the need for a critical viewpoint to understanding media narratives. In addition, the increasing use of short passives and get passives, alongside a general decline in passive voice, suggests evolving narrative strategies and possible changes in media consumption habits. The dominance of Anglo-Saxon media sources in the web-crawled Ukraine War corpus, constituting 78.99% of the data (Table 60), introduces significant bias into the analysis. This Western dominance raises questions about the objectivity and fairness of media reporting. Mahbubani (2022) argues that simplistic black-and-white perspectives from major Western outlets like THE ECONOMIST and THE NEW YORK TIMES often fail to capture the full complexity of geopolitical issues, particularly concerning countries like China and Russia. Future research should compare 87 Discussions findings with non-Western and non-English-speaking news sources to provide a more balanced view and better understand how different media systems represent global events. This study encourages further exploration of linguistic phenomena using authentic language, large datasets, and usage-based methods. Most importantly, it highlights the need for a comprehensive approach to media analysis that considers the full context of news reports from all angles, as well as the synchronic and diachronic analysis. Future research should build on this project by investigating how different representations of agency and responsibility impact the audience’s perceptions and attitudes across different cultures and languages. Last but not least, this project employs several computational tools, namely spaCy parser and static word embedding model, which come with obvious benefits but also limitations that can or cannot be mitigated. 7.1 Limitations of spaCy parser and static embedding models Although spaCy text parser offers the simplicity of use with robust algorithms through a simple pipeline, further configurations on the code are needed for certain purposes to achieve a deeper analysis, as well as comprehensive and insightful results. In addition, a smaller language model equipped in spaCy, while offering time and resource efficiency, may produce incorrect parsing results. One notable limitation of spaCy is its inability to handle referential dependencies beyond the sentence level. This issue includes identifying pronouns or numbers referring to entities in different sentences, or dealing with semantically inherent passive constructions, which Lehmann & Schneider (2009) term as NON-LOCAL subjects and objects. For instance, in the sentence He was released on 28 October 2022, and reached Ukrainian-controlled territory by 14 December, the PRONOUN He refers back to An American citizen from the previous sentence. This connection is beyond spaCys dependency boundary. Additionally, spaCy struggles with NUMERICAL SUBJECT without HEAD NOUN and INHERENT PASSIVE CONSTRUCTION. For example, in the sentence December 2022, of which 13 died and 50 were wounded between 1 January and 25 February 2022, phrases like 4,163 killed and 17,329 wounded are shortened forms of 4,163 who were killed and 17,329 who were wounded. These constructions are tagged with acl (adjectival clause) rather than reflecting a passive voice relationship. Similarly, it is cumbersome to count the frequencies of compound structures, or what Kübler et al. (2009) call COORDINATION. For example, the counting of subjects and verbs in the sentence Paul Urey and Dylan Healy were charged with mercenarism and captured by Russian forces. 88 (Back to Contents) Discussions may require tedious manual process as the compound subjects and verbs need to be segregated, resulting in four pairs: Urey_charged,Urey_captured,Healy_charged, and Healy_captured. Last but not least, spaCy offers different NLP models with difference levels of performance (Available trained pipelines for English 2023) accommodating various task requirements from a small model en_core_web_sm opt for a fast parsing with less accuracy (90-92% for parser) to the currently largest model with transformer architecture en_core_web_trf returning the most accurate results (94-95% for parser) but slower processing time and higher computational resource consumption. While the performance gap is minimal, a small model can cause inaccurate results as demonstrated in Figure 16 and Figure 17 below. The small model en_core_web_sm mistakenly tags areas as the head noun of the relative clause of whom 208 had been located whereas the transformer model en_core_web_trf correctly identifies civilians as the head noun. Using the small model can lead to an incorrect dependency parsing and compromise the accuracy. Figure 16: Parsed text by a small model, en_core_web_sm Figure 17: Parsed text by a transformer model, en_core_web_trf For static embedding models like Word2Vec, while they are simple and fast, the training algorithms allow them to learn only one fixed embedding for each word in a corpus used for training Jurafsky & Martin (2023: 120), so they cannot plot a coordinate on a semantic map based on the new context in which the word occurs. However for the categorization purpose in this study, the static embedding model offers clearer groupings as mentioned in Section 4.1.3. (Back to Contents) 89 Thompson, Sandra A. 1987. The passive in English: A Discourse Perspective. In Honor of Ilse Lehiste. Netherlands Phonetic Archives 6. 497–511. Villalibre, Eduardo C. 2015. Is the Get-passive Really that Adversative? Miscelánea: A Journal of English and American Studies (51). 13–26. DOI: 10.26754/ojs_misc/mj.20157166. Virtanen, Pauli, Gommers, Ralf, Oliphant, Travis E., Haberland, Matt, Reddy, Tyler, Cournapeau, David, Burovski, Evgeni, Peterson, Pearu, Weckesser, Warren, Bright, Jonathan, van der Walt, Stéfan J., Brett, Matthew, Wilson, Joshua, Millman, K. Jarrod, Mayorov, Nikolay, Nelson, Andrew R. J., Jones, Eric, Kern, Robert, Larson, Eric, Carey, CJ, Polat, lhan, Feng, Yu, Moore, Eric W., VanderPlas, Jake, Laxalde, Denis, Perktold, Josef, Cimrman, Robert, Henriksen, Ian, Quintero, E.A., Harris, Charles R., Archibald, Anne M., Ribeiro, Antônio H., Pedregosa, Fabian, van Mulbregt, Paul & Contributors, SciPy 1.0. 2001. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods 17(3). 261–72. Williams, Joseph M. & Rivers, William. 1983. Style: Ten lessons in clarity and grace. IEEE Transactions on Professional Communication PC-26(2). 88–9. DOI: 10.1109/TPC.1983. 6448695. Xiao, Richard, McEnery, Tony & Qian, Yufang. 2006. Passive Constructions in English and Chinese. Languages in Contrast 6(1). 109–49. DOI: 10.1075/lic.6.1.05xia.http://www.jbeplatform.com/content/journals/10.1075/lic.6.1.05xia. Ylä-Anttila, Tuukka, Eranti, Veikko & Kukkonen, Anna. 2022. Topic modeling for frame analysis: A study of media debates on climate change in India and USA. Global Media and Communication 18(1). 91–112. Zandvoort, Reinard W. 1972. A handbook of English grammar. 6th edn. London, UK: Longman. Zúñiga, Fernando & Kittilä, Seppo. 2019. Grammatical Voice (Cambridge Textbooks in Linguistics). UK: Cambridge University Press. DOI: 10.1017/9781316671399.https://www. cambridge.org/core/books/grammatical-voice/64740631FF2C9A2E61646C49328F222F. 96 (Back to Contents) Appendices Appendix 1: Corpora 1. Ukraine corpus 2. Leipzig corpora 3. Russian news corpora 3.1 Arctic 3.2 TASS 3.3 The Moscow Times Appendix 2: Source codes All source codes are stored in the Github repository.1Within the repository, the following codes can be found: 1. Extractions for a master data 1.1 Passive pairs 1.2 Frequencies of passive pairs 1.2.1 Frequencies among the passive pairs 1.2.2 Frequencies from an entire corpus 1.3 Observed frequencies (for the contingency table)2 1.4 Passive texts 2. Other extractions and visualizations 2.1 Proper noun 1Please contact [email protected] for any inquiries. 2Data arrangement of the input file has to be in 4 columns: 1. Subject_Verb_Pairs 2. s 3. v 4. O(sy_vy) where Subject_Verb_Pairs = the pairs in subject_verb format, s = Subjects, v = Verbs, O(sy_vy) = Occurrences of the pairs. 97 APPENDIX 2.2 Semantic map3 2.3 By-agents and modifiers 2.4 Active voice constructions 2.5 Beand get-passives 2.6 Topic modeling 3. Statistical calculations 3.1 Power analysis 3.2 p-values 3.2.1 p-value of t-score 3.2.2 p-value of Chi-squared test score 3.2.3 p-value of Fisher’s test score Appendix 3: News links from SketchEngine SketchEngine looks at 3 keywords at a time. Therefore, there are 3 sets of news links as follow: 1. Russo-Ukrainian War, Ukraine War, Russia’s war in Ukraine (33/33 selected) apnews.com/article/russia-dissent-crackdown-ordinary-people-c102c4f4f4fa7d11e5d3587257ecd864 apnews.com/article/russia-ukraine-war-what-you-need-to-know-2f94db1fa8cb96c47d7885efafbf4e79 en.wikipedia.org/wiki/Russian_invasion_of_Ukraine en.wikipedia.org/wiki/Russo-Ukrainian_War news.sky.com/story/russia-ukraine-war-latest-putin-election-live-updates-sky-news-blog-12541713 theconversation.com/no-the-west-is-not-to-blame-for-russias-war-in-ukraine-why-this-myth-and-others-are-sodifficult-to-dispel-226306 aljazeera.com/news/2022/2/28/russia-ukraine-crisis-in-maps-and-charts-live-news-interactive aljazeera.com/news/2024/3/16/russia-ukraine-war-list-of-key-events-day-752 bbc.com/news/war-in-ukraine bbc.com/news/world-europe-60506682 bloomberg.com/news/articles/2024-03-17/putin-takes-record-election-win-to-press-russia-s-war-in-ukraine cnbc.com/2024/03/14/ukraine-war-live-updates-latest-news-on-russia-and-the-war-in-ukraine.html cnn.com/europe/live-news/russia-ukraine-war-news-04-23-22/index.html cnn.com/europe/live-news/russia-ukraine-war-news-06-24-22/index.html cnn.com/europe/live-news/russia-ukraine-war-news-07-15-22/index.html cnn.com/europe/live-news/russia-ukraine-war-news-2-5-23-intl/index.html cnn.com/world/europe/ukraine ft.com/content/4351d5b0-0888-4b47-9368-6bc4dfbccbf5 iiss.org/online-analysis/online-analysis/2023/01/strategic-survey-2022-russias-war-in-ukraine/ nbcnews.com/news/world/farmers-protests-india-europe-ukraine-war-grain-prices-rcna143816 nejm.org/doi/full/10.1056/NEJMp2207415 npr.org/2023/02/22/1157106172/ukraine-russia-war-refugees-food-prices nytimes.com/article/ukraine-russia-war-timeline.html nytimes.com/live/2022/03/01/world/ukraine-russia-war nytimes.com/live/2022/05/20/world/russia-ukraine-war nytimes.com/news-event/ukraine-russia rand.org/latest/russia-ukraine.html 3This code is adapted from Parker (2022). Replace the code in https://colab.research.google.com/github/ futuremojo/nlp-demystified/blob/main/notebooks/nlpdemystified_word_vectors.ipynb in the section where the Principal Components Analysis (PCA) is implemented. 98 (Back to Contents) APPENDIX reuters.com/world/europe/blood-billions-cost-russias-war-ukraine-2023-08-23/ reuters.com/world/ukraine-russia-war/ theguardian.com/world/live/2024/mar/15/russia-ukraine-war-russia-election-putin-macron-zelenskiy-belgorodmoscow-live-updates theguardian.com/world/series/ukraine-live/latest vox.com/22970918/russia-war-in-ukraine-explained washingtonpost.com/world/2024/03/20/czech-republic-slovakia-split-prague-russia-ukraine/ 2. Russo-Ukrainian War, Russia-Ukraine war, Russia’s war in Ukraine (31/31 selected) apnews.com/article/russia-dissent-crackdown-ordinary-people-c102c4f4f4fa7d11e5d3587257ecd864 apnews.com/article/russia-ukraine-war-putin-nuclear-weapons-82ced2419d93ae733161b56fbd9b477d apnews.com/article/russia-ukraine-war-what-you-need-to-know-2f94db1fa8cb96c47d7885efafbf4e79 en.wikipedia.org/wiki/Russo-Ukrainian_War foreignpolicy.com/2024/03/19/technology-ai-drones-stalemate-ukraine-russia-manpower/ news.sky.com/story/russia-ukraine-war-latest-putin-election-live-updates-sky-news-blog-12541713 aljazeera.com/news/2022/2/28/russia-ukraine-crisis-in-maps-and-charts-live-news-interactive aljazeera.com/news/2024/3/16/russia-ukraine-war-list-of-key-events-day-752 bbc.com/news/war-in-ukraine bbc.com/news/world-europe-60506682 bloomberg.com/news/articles/2024-03-17/putin-takes-record-election-win-to-press-russia-s-war-in-ukraine cnbc.com/2024/03/14/ukraine-war-live-updates-latest-news-on-russia-and-the-war-in-ukraine.html cnn.com/europe/live-news/russia-ukraine-war-news-04-23-22/index.html cnn.com/europe/live-news/russia-ukraine-war-news-07-15-22/index.html cnn.com/europe/live-news/russia-ukraine-war-news-2-5-23-intl/index.html cnn.com/world/europe/ukraine dw.com/en/russias-war-in-ukraine/t-60931789 iiss.org/online-analysis/online-analysis/2023/01/strategic-survey-2022-russias-war-in-ukraine/ nejm.org/doi/full/10.1056/NEJMp2207415 npr.org/2023/02/22/1157106172/ukraine-russia-war-refugees-food-prices nytimes.com/article/ukraine-russia-war-timeline.html nytimes.com/live/2022/03/01/world/ukraine-russia-war nytimes.com/news-event/ukraine-russia rand.org/latest/russia-ukraine.html reuters.com/world/europe/blood-billions-cost-russias-war-ukraine-2023-08-23/ reuters.com/world/ukraine-russia-war/ theguardian.com/world/2024/feb/18/russia-ukraine-war-at-a-glance-what-we-know-on-day-725 theguardian.com/world/live/2024/mar/15/russia-ukraine-war-russia-election-putin-macron-zelenskiy-belgorodmoscow-live-updates theguardian.com/world/series/ukraine-live/latest vox.com/2022/2/23/22948534/russia-ukraine-war-putin-explosions-invasion-explained vox.com/22970918/russia-war-in-ukraine-explained Russo-Ukrainian War, Russia-Ukraine war, Ukraine War (37/37 selected) apnews.com/article/russia-ukraine-war-drone-attack-f0e717ace1b9a4c286fe1d2e112d74fe apnews.com/article/russia-ukraine-war-putin-nuclear-weapons-82ced2419d93ae733161b56fbd9b477d en.wikipedia.org/wiki/Casualties_of_the_Russo-Ukrainian_War en.wikipedia.org/wiki/Outline_of_the_Russo-Ukrainian_War en.wikipedia.org/wiki/Russo-Ukrainian_War news.sky.com/video/ukraine-missile-attacks-hit-kyiv-after-ukrainian-rockets-hit-400-miles-inside-russiaukraine-war-13099401 warontherocks.com/understanding-the-russo-ukrainian-war-a-guide-from-war-on-the-rocks/ aljazeera.com/news/2022/2/28/russia-ukraine-crisis-in-maps-and-charts-live-news-interactive aljazeera.com/news/2024/3/16/russia-ukraine-war-list-of-key-events-day-752 aljazeera.com/news/2024/3/18/russia-ukraine-war-list-of-key-events-day-753 aljazeera.com/news/2024/3/18/ukraine-debates-mobilising-more-men-to-fight-russia-after-two-years-of-war bbc.com/news/explainers-62902029 (Back to Contents) 99 APPENDIX bbc.com/news/war-in-ukraine bbc.com/news/world-europe-60504334 bbc.com/news/world-europe-60506682 bbc.com/news/world-europe-68590088 bloomberg.com/news/articles/2024-03-17/putin-takes-record-election-win-to-press-russia-s-war-in-ukraine cfr.org/global-conflict-tracker/conflict/conflict-ukraine cnbc.com/2024/03/15/russia-ukraine-live-updates.html cnn.com/europe/live-news/russia-ukraine-war-news-2-5-23-intl/index.html cnn.com/world/europe/ukraine cnn.com/world/live-news/russia-ukraine-war-12-29-23/index.html ft.com/content/4351d5b0-0888-4b47-9368-6bc4dfbccbf5 irishtimes.com/world/europe/2024/03/20/russia-ukraine-war-latest/ nato.int/cps/en/natohq/topics_192648.htm nato.int/docu/review/articles/2022/07/07/the-consequences-of-russias-invasion-of-ukraine-for-internationalsecurity-nato-and-beyond/index.html nytimes.com/2023/08/18/us/politics/ukraine-russia-war-casualties.html nytimes.com/2024/03/05/world/europe/ukraine-war-russia-planes.html nytimes.com/2024/03/12/world/europe/ukraine-drone-russia-jamming.html nytimes.com/article/ukraine-russia-war-timeline.html nytimes.com/interactive/2022/world/europe/ukraine-maps.html nytimes.com/news-event/ukraine-russia rand.org/latest/russia-ukraine.html theguardian.com/world/2024/mar/14/ukraine-russia-war-update-today-aid-putin-zelenskiy theguardian.com/world/live/2024/mar/13/ukraine-russia-war-live-vladimir-putin-volodymyr-zelenskiy-nato theguardian.com/world/series/ukraine-live/latest vox.com/2022/2/23/22948534/russia-ukraine-war-putin-explosions-invasion-explained 3. Russia-Ukraine war, Ukraine War, Russia’s war in Ukraine (37/37 selected) apnews.com/article/russia-dissent-crackdown-ordinary-people-c102c4f4f4fa7d11e5d3587257ecd864 apnews.com/article/russia-ukraine-war-drone-attack-f0e717ace1b9a4c286fe1d2e112d74fe foreignpolicy.com/2024/03/19/technology-ai-drones-stalemate-ukraine-russia-manpower/ news.sky.com/story/ukraine-russia-war-news-putin-zelenskyy-latest-live-updates-12541713?wpmobileexternal= true aljazeera.com/news/2022/2/28/russia-ukraine-crisis-in-maps-and-charts-live-news-interactive aljazeera.com/news/2022/8/24/timeline-six-months-of-russias-war-in-ukraine aljazeera.com/news/2024/3/20/russia-ukraine-war-list-of-key-events-day-755 atlanticcouncil.org/blogs/new-atlanticist/six-months-twenty-three-lessons-what-the-world-has-learned-fromrussias-war-in-ukraine/ bbc.com/news/war-in-ukraine bbc.com/news/world-europe-60506682 bbc.com/news/world-europe-68573646 bbc.com/news/world-europe-68590088 bloomberg.com/news/articles/2024-03-17/putin-takes-record-election-win-to-press-russia-s-war-in-ukraine cnbc.com/2024/03/13/ukraine-war-live-updates-latest-news-on-russia-and-the-war-in-ukraine.html cnbc.com/2024/03/14/ukraine-war-live-updates-latest-news-on-russia-and-the-war-in-ukraine.html cnn.com/europe/live-news/russia-ukraine-war-news-04-23-22/index.html cnn.com/europe/live-news/russia-ukraine-war-news-04-29-23/index.html cnn.com/europe/live-news/russia-ukraine-war-news-06-24-22/index.html cnn.com/europe/live-news/russia-ukraine-war-news-08-06-23/index.html cnn.com/europe/live-news/russia-ukraine-war-news-2-5-23-intl/index.html cnn.com/world/europe/ukraine iea.org/topics/russias-war-on-ukraine iiss.org/online-analysis/online-analysis/2023/01/strategic-survey-2022-russias-war-in-ukraine/ nejm.org/doi/full/10.1056/NEJMp2207415 nytimes.com/2024/03/05/world/europe/ukraine-war-russia-planes.html nytimes.com/article/ukraine-russia-war-timeline.html nytimes.com/news-event/ukraine-russia 100 (Back to Contents) APPENDIX pbs.org/newshour/world/1-year-after-the-invasion-began-a-timeline-of-russias-war-in-ukraine rand.org/latest/russia-ukraine.html reuters.com/world/europe/blood-billions-cost-russias-war-ukraine-2023-08-23/ theguardian.com/world/2022/mar/17/russias-war-in-ukraine-complete-guide-in-maps-video-and-pictures theguardian.com/world/2024/mar/15/ukraine-war-briefing-russian-forces-fending-off-attacks-by-pro-kyivfighters usip.org/publications/2024/03/ukraine-war-takes-toll-russia vox.com/2022/2/23/22948534/russia-ukraine-war-putin-explosions-invasion-explained vox.com/22970918/russia-war-in-ukraine-explained vox.com/22989379/russia-ukraine-war-putin-zelenskyy-us-nato-explainer-questions washingtonpost.com/world/2024/03/20/czech-republic-slovakia-split-prague-russia-ukraine/ Appendix 4: Topic keywords and scores Leipzig corpora Table 43: Topic keywords and scores in the Leipzig corpus 2014 Topic_Lpz_14 Score Norm. Score Keywords Education 1920 596.36 school, student, teacher, education, college, class, teach, grade, exam, district Russia-Ukraine Conflict 1789 555.67 ukraine, russia, russian, ukrainian, putin, crimea, moscow, kiev, separatist, donetsk Israel-Palestine Conflict 1619 502.87 israel, israeli, gaza, palestinian, hamas, palestinians, jewish, rocket, jerusalem, israelis Gun Violence 1479 459.38 gun, shoot, shooting, firearm, officer, wound, gunshot, bullet, knife, kill Film Industry 1249 387.95 film, movie, actor, nominate, oscar, star, comedy, episode, actress, drama Medical Emergencies 1199 372.42 hospital, ambulance, injury, stable, condition, transport, take, airlift, critical, medical China Relations 1163 361.23 china, chinese, beijing, hong, kong, taiwan, japan, abe, japanese, zhou Music 965 299.73 music, song, album, band, dance, grammy, singer, artist, guitar, musical Fire and Firefighting 927 287.93 fire, firefighter, blaze, flame, burn, extinguish, wildfire, crew, arson, destroy Space Exploration 741 230.16 space, mars, moon, nasa, earth, spacecraft, comet, planet, orbit, rover Parenting 732 227.36 bear, daughter, baby, son, father, grizzly, live, mother, raise, kan Weather and Natural Dis. 649 201.58 snow, temperature, wind, rain, storm, forecast, weather, hurricane, inch, snowfall Sexual Violence 595 184.81 sexual, rape, assault, sexually, sex, abuse, indecent, count, girl, offender Soccer 593 184.19 goalkeeper, brazil, goal, cup, striker, footed, penalty, minute, madrid, shot Pets 587 182.33 dog, animal, pet, cat, cruelty, puppy, owner, humane, euthanize, kennel Ebola Outbreak 586 182.01 ebola, outbreak, virus, africa, leone, liberia, infect, sierra, patient, guinea Smartphones 561 174.25 iphone, apple, android, tablet, samsung, smartphone, ipad, device, galaxy, blackberry Protests 536 166.48 protest, protester, demonstration, demonstrator, rally, activist, arrest, peaceful, riot Table 44: Topic keywords and scores in the Leipzig corpus 2015 Topic_Lpz_15 Score Norm. Score Keywords Education 1724 514.51 school, student, teacher, education, college, university, class, grade, teach, scholarship Gun Violence 1447 431.84 gun, shooting, shoot, gunshot, shot, gunfire, officer, fatally, firearm, bullet Fire and Firefighting 1201 358.43 fire, firefighter, flame, wildfire, blaze, burn, extinguish, engulf, firework, crew Russia-Ukraine Conflict 1186 353.95 ukraine, russia, russian, ukrainian, putin, moscow, donetsk, minsk, vladimir, crimea Space Exploration 1073 320.23 mars, nasa, earth, moon, pluto, space, planet, spacecraft, comet, rover Middle East Conflict 940 280.53 syria, iraq, isis, syrian, iraqi, islamic, assad, al, ramadi, sunni Religion 767 228.90 pope, church, francis, vatican, faith, religion, catholic, priest, bishop, religious Sexual Violence 754 225.02 sexual, rape, sexually, sex, assault, abuse, pornography, offender, child, prostitution Continued on next page (Back to Contents) 101 APPENDIX Topic_Lpz_15 Score Norm. Score Keywords Parenting 707 211.00 bear, baby, son, daughter, child, mrs, birth, congenital, mother, raise China Relations 661 197.27 china, chinese, taiwan, beijing, hong, xi, kong, communist, taiwanese, wang Greek Crisis 627 187.12 greece, greek, bailout, tsipras, euro, athens, eurozone, creditor, debt, imf Israel-Palestine Conflict 613 182.94 israeli, palestinian, israel, palestinians, israelis, gaza, jerusalem, hamas, jewish, stabbing Paris Attacks 591 176.38 paris, french, france, belgium, belgian, attack, brussels, abaaoud, terror, raid Crime 568 169.51 arrest, theft, warrant, burglary, charge, felony, mischief, shoplifting, misdemeanor, county Cybersecurity 514 153.40 password, hacker, hack, encrypt, encryption, malware, privacy, breach, vulnerability Weather & Nat. Dis. 496 148.03 snow, rain, temperature, weather, wind, forecast, storm, expect, inch, centimetre Music 491 146.53 album, song, band, music, jazz, lyric, guitar, singer, musician, perform Film Industry 151 45.06 film, movie, sequel, filmmaker, documentary, cinema, murray, quentin, lovefilm, elstree Table 45: Topic keywords and scores in the Leipzig corpus 2016 Topic_Lpz_16 Score Norm. Sc. Keywords Education and Schooling 3544 1056.57 school, student, teacher, college, scholarship, university, grade, graduate, elementary Parenting 2039 607.89 bear, daughter, late, son, baby, july, raise, mrs, mary, sr Legal and Court Matters 1707 508.91 court, trial, jury, appeal, judge, case, attorney, guilty, lawyer, supreme Religion and Clergy 1409 420.07 god, pope, faith, jesus, bishop, priest, church, christ, religious, pastor Fire and Firefighting 1150 342.85 fire, firefighter, burn, wildfire, firework, blaze, flame, smoke, department, extinguish Music 1042 310.65 music, song, album, band, guitar, musician, musical, orchestra, rock, jazz Art and Museums 898 267.72 art, artist, museum, painting, paint, gallery, exhibit, exhibition, artwork, mural Death and Obituaries 792 236.12 precede, death, brother, sister, parent, husband, son, predecease, grandson, father Sports and Games 782 233.14 yard, touchdown, goal, foul, penalty, ball, quarterback, shot, defensive, score Journalism and Publishing 734 218.83 newspaper, news, editor, publish, journalism, reporter, magazine, story, print, article Cooking and Food 706 210.48 sauce, dish, fry, cheese, bread, cook, recipe, grill, slice, meat Weather and Natural Dis. 648 193.19 weather, temperature, rain, hurricane, storm, forecast, snow, wind, winter, tornado Film Industry 616 183.65 film, movie, actor, screen, festival, star, cinema, filmmaker, documentary, hollywood Medical and Emergency 571 170.23 hospital, ambulance, injury, transport, airlift, treat, medical, emergency, rush, treatment Charity and Volunteering 559 166.65 donation, volunteer, donate, charitable, charity, fundraising, nonprofit, donor, organization Funerals and Memorial 556 165.76 funeral, officiate, chapel, rev, service, hold, church, baptist, home, pastor Fashion and Clothing 535 159.50 dress, wear, shoe, costume, fashion, clothing, shirt, clothe, fabric, leather Fishing and Wildlife 526 156.82 fish, fishing, fishery, salmon, bait, trout, catch, angler, fisherman, wildlife Alcohol and Beverages 523 155.92 wine, beer, bottle, brewery, winery, liquor, grape, drink, brew, beverage Pets and Animals 508 151.45 dog, pet, animal, cat, puppy, leash, shelter, owner, pup, breed Agriculture and Farming 486 144.89 farmer, crop, agriculture, seed, corn, agricultural, plant, farming, farm, fertilizer Israel-Palestine Conflict 425 126.71 israel, israeli, palestinian, gaza, hamas, israelis, netanyahu, jerusalem, palestine Russia-Ukraine Conflict 164 48.89 russia, putin, russian, soviet, ukraine, moscow, kremlin, vladimir, minsk, russians Gun Violence 62 18.48 gun, nra, firearm, control, violence, law, federal, restricted, rifle, outlaw Table 46: Topic keywords and scores in the Leipzig corpus 2017 Topic_Lpz_17 Score Norm. Score Keywords Music 1910 550.21 music, song, album, band, dance, musician, musical, concert, singer, perform Education 1802 518.98 school, teacher, student, education, college, university, scholarship, graduate, class, program Film Industry 1790 515.55 film, movie, actor, star, wars, marvel, character, batman, role, cinema Gun Violence 1090 314.05 shoot, shooting, gun, firearm, ammunition, kill, rifle, fatally, wound, shot Medical Emergencies 966 278.19 hospital, injury, ambulance, rush, transport, condition, critical, threaten, medical, take Fire and Firefighting 905 260.67 fire, firefighter, blaze, wildfire, flame, burn, extinguish, firework, crew, damage Parenting 852 245.43 bear, daughter, late, baby, son, july, grizzly, sr, mae, april Weather and Natural Dis. 647 186.35 hurricane, wind, storm, rain, irma, weather, temperature, snow, mph, forecast Obituaries 639 184.05 precede, sister, brother, survive, grandchild, death, parent, husband, daughter, niece Continued on next page 102 (Back to Contents) APPENDIX Topic_Lpz_17 Score Norm. Score Keywords Art 637 183.48 art, museum, painting, artist, exhibit, paint, artwork, gallery, exhibition, sculpture Gender Issues 609 175.37 gender, transgender, gay, woman, lgbt, lgbtq, female, sex, male, feminist Pets 575 165.63 dog, animal, pet, cat, rescue, shelter, owner, puppy, humane, cruelty Culinary 554 159.64 dish, sauce, cook, cheese, chicken, flavor, fry, chef, ingredient, salad Prison 521 150.06 prison, sentence, inmate, parole, jail, probation, imprisonment, prisoner, convict, serve Israel-Palestine Conflict 344 99.08 israel, israeli, palestinian, palestinians, jerusalem, netanyahu, hamas, gaza, palestine, israelis Russia-Ukr. Confl. 340 97.89 russia, putin, moscow, ukraine, russians, vladimir, trump, crimea, election Table 47: Topic keywords and scores in the Leipzig corpus 2018 Topic_Lpz_18 Score Norm. Score Keywords Weather and Natural Dis. 2290 653.67 water, rain, snow, flood, hurricane, storm, weather, wind, flooding, temperature Food and Dining 2250 642.25 food, restaurant, beer, meal, wine, dish, drink, menu, coffee, eat Music 2101 599.72 music, song, album, band, dance, singer, perform, musical, concert, musician Education and Schooling 1875 535.21 school, student, scholarship, education, teacher, college, exam, university, academic Soccer and Sports 1421 405.62 chelsea, goal, arsenal, league, manchester, mourinho, liverpool, penalty, referee, midfielder American Football 1206 344.25 quarterback, coach, draft, nfl, yard, defensive, offensive, pick, brady, qb Fire and Firefighting 1203 343.39 fire, firefighter, flame, burn, wildfire, blaze, smoke, firework, crew, extinguish Parenting 1069 305.14 bear, daughter, late, son, baby, april, cub, raise, william, july Israel-Palestine Conflict 1006 287.16 israel, israeli, gaza, jews, jewish, palestinian, palestinians, hamas, netanyahu, jerusalem Elections and Voting 944 269.46 voter, ballot, election, vote, voting, polling, republican, poll, candidate, democrats Religion and Clergy 913 260.61 church, god, priest, bishop, pope, diocese, catholic, jesus, vatican, archbishop Nigerian Politics 861 245.77 apc, buhari, nigeria, pdp, muhammadu, nigerians, president, governor, chairman, atiku Energy and Resources 783 223.50 oil, gas, pipeline, energy, coal, solar, mining, fuel, crude, electricity Russia-Ukr. Confl. 666 190.11 russia, putin, moscow, skripal, vladimir, ukraine, novichok, agent, sergei Film Industry 606 172.98 film, movie, actor, cast, theater, director, script, filmmaker, sequel, direct Prisons and Sentencing 574 163.85 prison, sentence, jail, inmate, parole, probation, prisoner, imprisonment, convict, serve Drugs and Addiction 525 149.86 drug, opioid, prescription, overdose, addiction, fentanyl, substance, fda, medication Pets and Animals 513 146.43 dog, animal, pet, cat, puppy, cruelty, leash, vet, shelter, kitten China and Trade 503 143.58 china, tariff, chinese, trade, beijing, xi, huawei, steel, meng, jinping Gun Violence 309 88.20 shoot, shooting, fatally, wound, gunfire, gunshot, man, officer, kill, chicago Table 48: Topic keywords and scores in the Leipzig corpus 2019 Topic_Lpz_19 Score Norm. Score Keywords American Football 2487 719.87 quarterback, game, yard, nfl, season, coach, draft, injury, touchdown, defensive Music 2155 623.77 music, song, album, band, dance, singer, musical, concert, sing, musician Education 2020 584.69 school, teacher, student, education, college, university, exam, scholarship, class, grade Cryptocurrency 1402 405.81 cryptocurrency, btc, exchange, token, coin, purchase, idex, buy, following, cryptopia Housing Market 1197 346.47 housing, rent, building, property, lease, demolish, apartment, land, rental, build Social Media 1050 303.92 facebook, twitter, media, social, news, tweet, publish, article, journalist, editor Fire and Firefighting 1006 291.19 fire, firefighter, burn, blaze, wildfire, flame, extinguish, firework, crew, smoke Transportation 994 287.72 bridge, traffic, bus, road, lane, route, reopen, highway, close, train Culinary Arts 957 277.01 chef, dish, menu, restaurant, fry, cook, chicken, dinner, flavor, food Brexit 915 264.85 brexit, eu, corbyn, johnson, labour, boris, tory, britain, referendum, backstop Parenting 900 260.51 bear, baby, late, son, daughter, mae, ruth, william, april, helen Medical Emergencies 659 190.75 hospital, injury, ambulance, transport, threaten, condition, airlift, medical, paramedic, take Police Investigations 626 181.20 police, information, stopper, contact, crime, detective, anonymously, incident, whereabouts Obituaries 604 174.83 precede, brother, sister, survive, grandchild, parent, death, husband, daughter, predecease Pets 543 157.17 dog, pet, animal, cat, cruelty, puppy, kitten, shelter, owner, rescue Religion 532 153.99 priest, church, god, catholic, bishop, pope, diocese, vatican, faith, pastor Continued on next page (Back to Contents) 103 APPENDIX Topic_Lpz_19 Score Norm. Score Keywords Middle East Conflict 513 148.49 syria, kurdish, turkish, turkey, syrian, baghdadi, isis, al, erdogan, fighter Weather and Nat. Dis. 440 127.36 wind, snow, temperature, rain, forecast, weather, storm, shower, mph, thunderstorm Gun Violence 347 100.44 shoot, shooting, gunshot, wound, fatally, kill, bullet, gunman, chicago, officer Israel-Palestine Conflict 306 88.57 israel, palestinian, gaza, israeli, palestinians, hamas, israelis, jerusalem, palestine, jihad Film Industry 248 71.78 film, kapoor, khan, directorial, actor, salman, starrer, bollywood, ranveer, films Russia-Ukr. Confl. 198 57.31 ukraine, giuliani, ukrainian, yovanovitch, zelensky, biden, trump, volodymyr, rudy, aid Table 49: Topic keywords and scores in the Leipzig corpus 2020 Topic_Lpz_20 Score Norm. Score Keywords US Election 6396 1752.56 election, ballot, vote, voter, biden, trump, elect, president, party, candidate Education 2899 794.35 school, student, education, teacher, learning, class, college, exam, university, grade Sanity Measures 1636 448.28 mask, wear, face, covering, glove, disinfect, distancing, sanitize, sanitizer, clean Gun Violence 1356 371.56 arrest, shoot, shooting, firearm, gunshot, wound, robbery, gun, charge, man Music 1223 335.11 song, album, music, singer, band, rapper, musician, musical, lyric, artist Fire and Firefighting 1168 320.04 fire, firefighter, burn, bushfire, flame, wildfire, blaze, smoke, extinguish, destroy Coronavirus 1058 289.90 coronavirus, novel, infect, case, confirm, test, patient, positive, outbreak, infection Weather and Natural Dis. 975 267.16 rain, storm, flood, weather, hurricane, snow, flooding, wind, temperature, forecast Court 965 264.42 court, appeal, trial, judge, lawsuit, supreme, lawyer, hearing, attorney, jury Parenting 799 218.93 bear, baby, son, daughter, late, father, raise, helen, mother, cub Quarantine 738 202.22 quarantine, hotel, arrival, self, traveller, mandatory, contact, isolation, positive, day Middle East Conflict 721 197.56 iran, iranian, iraq, soleimani, tehran, syria, baghdad, iraqi, syrian, turkish Fashion 660 180.85 dress, hair, shirt, wear, shoe, fashion, clothe, clothing, leather, jacket Vaccine 655 179.48 vaccine, vaccinate, vaccination, dose, pfizer, moderna, trial, develop, administer, biontech Religion 627 171.80 church, jesus, god, prayer, worship, religious, faith, bishop, pope, pastor Pets 619 169.61 dog, animal, pet, zoo, cat, elephant, tiger, puppy, veterinary, owner Soccer 613 167.97 arsenal, liverpool, manchester, midfielder, chelsea, goal, penalty, barcelona, madrid, league Virus 568 155.64 virus, infect, spread, symptom, test, transmission, positive, expose, infected, contagious Gaming 567 155.36 game, console, xbox, playstation, nintendo, sony, gaming, pc, release, gamer Cryptocurrency 564 154.54 btc, cryptocurrency, exchange, token, coin, purchase, buy, idex, following, coinexchange Traffic 554 151.80 road, traffic, lane, street, parking, highway, park, motorist, avenue, intersection Lockdown 549 150.43 lockdown, lift, restriction, impose, ease, lock, shutdown, partial, strict, extend American Football 548 150.16 draft, quarterback, yard, nfl, touchdown, brady, patriots, pick, defensive, cornerback Racism 534 146.32 racism, black, racial, slavery, racist, slave, white, color, discrimination, african Israel-Palestine Conflict 370 101.38 israel, palestinian, israeli, netanyahu, gaza, palestinians, hamas, arab, gantz, israelis Film Industry 259 70.97 khan, film, actor, salman, bollywood, hindi, kapoor, hai, telugu, vijay Russia-Ukr. Confl. 70 19.18 putin, russia, russian, moscow, vladimir, kgb, ukraine, soviet, sergey, crimea Table 50: Topic keywords and scores in the Leipzig corpus 2023 Topic_Lpz_23 Score Norm. Score Keywords Music 1889 538.85 music, song, album, band, concert, musical, dance, perform, singer, artist Education 1629 464.69 school, education, student, teacher, college, exam, university, pupil, teach, primary Israel and Palestine 1608 458.70 israel, gaza, israeli, hamas, palestinian, palestinians, jewish, hostage, netanyahu, jews Sports (Soccer) 1188 338.89 league, chelsea, manchester, club, arsenal, premier, united, midfielder, liverpool, newcastle Crime 1018 290.39 arrest, murder, charge, suspicion, suspect, warrant, connection, custody, man, assault Food and Beverage 849 242.19 sauce, cheese, dish, cook, meal, bread, menu, flavor, pie, onion Fire and Firefighting 812 231.63 fire, firefighter, wildfire, blaze, crew, extinguish, flame, firework, burn, rescue Water and Weather 807 230.20 water, flood, flooding, river, dam, sewage, rain, sewer, storm, reservoir Economic Growth 803 229.06 growth, revenue, stock, margin, offset, undervalue, market, index, valuation, portfolio Weather Conditions 717 204.53 rain, temperature, weather, forecast, wind, snow, storm, warning, thunderstorm, expect Continued on next page 104 (Back to Contents) APPENDIX Topic_Lpz_23 Score Norm. Score Keywords Pets and Animals 686 195.69 dog, pet, animal, cat, breed, puppy, owner, cruelty, vet, kennel Events and Festivals 679 193.69 event, festival, invite, celebration, celebrate, hold, anniversary, attend, host, annual Ukr.-Russia Confl. 597 170.30 ukraine, russian, putin, russia, ukrainian, moscow, invasion, wagner, war, vladimir NFL Football 589 168.02 draft, quarterback, yard, nfl, pick, touchdown, steelers, receiver, defensive, offensive Drug-related Activities 576 164.31 cannabis, drug, cocaine, marijuana, possession, seize, methamphetamine, heroin, substance Maritime Activities 558 159.17 vessel, ship, boat, lifeboat, sea, coastguard, submarine, coast, rescue, maritime Art and Museums 541 154.33 museum, artist, art, painting, paint, gallery, exhibition, artwork, exhibit, sculpture Religion and Church 538 153.47 church, god, pope, bishop, priest, vatican, jesus, holy, prayer, francis Movies and Cinema 525 149.76 film, movie, theater, actor, festival, cinema, cast, release, role, filmmaker Fashion and Clothing 512 146.05 wear, dress, shoe, shirt, jacket, black, fabric, pant, fashion, pair Parenting 497 141.77 bear, raise, daughter, son, late, william, mae, mary, york, twin [Return to Section 5.2.5 Topics in news reports] Russian news corpora Table 51: Topic keywords and scores in the Arctic corpus Topic_Arctic Score Norm. Score Keywords Icebreakers 75 368.03 icebreaker, ice, platform, nuclear, floe, ship, resistant, helicopter, shipyard, petersburg Ports and Routes 50 245.36 port, route, sea, cargo, ton, northern, ship, terminal, transport, million Budget and Finance 46 225.73 ruble, billion, allocate, million, budget, investment, purpose, federal, estimate, total Polar Wildlife 41 201.19 bear, polar, island, animal, find, mammoth, count, scientist, species, walrus Legal Framework 40 196.29 law, draft, land, submit, duma, plot, relevant, adopt, house, state Education 40 196.29 student, teacher, university, institution, school, profession, educational, training, research Gov. & Dev. 38 186.47 east, far, ministry, development, russian, arctic, corporation, share, agency, minister Climate Change 38 186.47 climate, temperature, ice, change, melt, greenland, record, lake, glacier, coast Energy Resources 38 186.47 gas, deposit, oil, lng, produce, mineral, production, fuel, coal, promising Economic Development 37 181.56 economic, development, job, arctic, socioeconomic, worker, zone, far, economy, human Arctic Council 37 181.56 council, foreign, arctic, ministerial, cooperation, russia, secretary, reykjavik, denmark Future Expectations 37 181.56 expect, step, time, accept, vilyuisky, august, end, population, complete, applicant Arctic Geography 35 171.75 island, archipelago, zemlya, novaya, franz, josef, geographical, land, find, admiral Forums and Events 27 132.49 forum, hold, event, international, petersburg, st, tourist, festival, break, arctic Indigenous Peoples 24 117.77 indigenous, people, language, ethnic, small, cultural, group, culture, linguistic, forum Table 52: Topic keywords and scores in TASS corpus Topic_TASS Score Norm. Score Keywords Russian Economy 138 628.18 russia, tax, economy, sanction, sell, income, north, russian, payment, spending Arts and Culture 83 377.82 film, director, theater, artist, museum, soviet, book, life, dance, performance Climate and Wildfires 73 332.30 fire, wildfire, climate, forest, region, temperature, arctic, change, year, degree Cuisine 67 304.98 meat, dish, porridge, macaroni, serve, cream, guryev, cook, fish, oven Conflict and Casualties 48 218.50 injure, kill, wound, shelling, people, gladkov, shrapnel, die, say, region Flooding and Rivers 48 218.50 flood, river, orenburg, dam, water, orsk, ural, level, home, flooding Arrests and Trials 47 213.94 arrest, charge, sentence, year, prison, trial, detention, favorskaya, detain, evan Environmental Issues 45 204.84 fuel, submarine, waste, uranium, mineral, extraction, reactor, environmental, project, coal Political Appointments 45 204.84 minister, appoint, putin, governor, deputy, term, presidential, replace, president, new Chechnya and Kadyrov 38 172.98 kadyrov, chechnya, ramzan, chechen, kadyrovtsy, akhmat, appoint, alaudinov, assassinate Drone Attacks 32 145.66 drone, target, region, attack, destroy, defense, ukrainian, ministry, say, strike Continued on next page (Back to Contents) 105 APPENDIX Table 54: Proper nouns in the Ukraine War corpus Category Sub-cat_1 Sub-cat_2 Proper Noun Subject Verbs % People Ukrainian Government and Military Officials - Yanukovych (Former President), Serhiy (Luhansk regional military administration), Nikulin (police officer), Savluchenko (head of the Department of Youth and Sports of Kherson), Zelensky (Current President), Yushchenko (Former President), Budanov (military leader) lined, killed, released, declared, removed, poisoned, born, discussed, expected 5.23% Athlete - Svitolina (Wimbledon semi-finalist) ranked 0.65% Russian Government and Military Officials - Putin (Current President), Medvedev (Former President), Ordash (Russian charge d’affaires in Warsaw), Nadezhdin (liberal politician) expected, fixated, held, interviewed, isolated, removed, tried, trusted, described, handed, barred 8.50% Activists and Advocates - Petrova (anti-war activist), Rumyantsev (anti-war activist), Navalny (Russia opposition), Volkov (Chief of Staff of AntiCorruption Foundation), Dugina (pro-war pundit) arrested, committed, declared, escorted, sentenced, attacked, taken, charged, known, poisoned, treated, killed 15.03% Poets and Artists - Kamardin (poet), Shtovba (poet), Gergiev (conductor) given, sentenced 4.58% Athlete - Mazepin (F1 driver) allowed 0.65% American - Biden (President), McCann (reporter and graphics editor) interrupted, arrested 1.31% British - Healy (humanitarian volunteer), Urey (humanitarian volunteer), Shapps (British Defense Secretary) charged, warned, captured, diagnosed 3.92% Others - Shekharappa (Indian student), Guemy (French street artist), Guterres (UN Secretary-General from Portugal) known, expected, received, killed 1.96% Locations Ukraine City Dnieper, Donbas, Bucha, Kyiv, Luhansk, Severodonetsk, Donetsk, Kherson, Mykolaiv, Amvrosiivka, Vinnytsia, Lysychansk occupied, subjected, cut, considered, plagued, liberated, hit, left, stretched, hit, shelled, abandoned, attacked 16.34% Country Ukraine allowed, led, met, ruled, spared, undertaken Nationality Ukrainian displaced, held, killed, released Russia City Samara, Moscow believed, focused, mired, designed 30.72% Country Russia absorbed, allowed, believed, bound, brought, designated, expected, forced, held, led, offended, prepared, recognized, reported, represented, required, stripped, thought Nationality Russian arrested, swept, thwarted Disputed area - Abkhazia, Crimea recognised, invaded 1.31% Others - UK, US, Moldova attacked, launched, expected, founded 2.61% Organizations - NATO, Gazprom, Facebook, Arconic forced, hit, blocked, built, created 3.27% Terms and Concepts - Neptune, POWs, SIG, UAV, Bavovna used, introduced, held, killed, manipulated, damaged, hit 3.92% Total 100.00 [Return to Section 5.1.2.1 Proper nouns categorization in the Ukraine War corpus] 112 (Back to Contents) APPENDIX Table 55: Proper noun occurrences by years in the Leipzig corpora Category & Sub-category Prop. N. 2014 2015 2016 2017 2018 2019 2020 2023 %word %sub-cat. %cat. People Ukrainian Officers Zelensky 24.40 66.60 58.59 Yushchenko 6.41 4.13 Yanukovych 14.44 12.83 16.30 28.05 Serhiy 6.41 4.13 Nikulin 7.93 5.11 %sub-category 9.30 16.51 5.11 26.20 42.88 100.00 10.39 Russian Officers Putin 324.94 218.90 56.89 95.38 142.65 8.13 53.27 133.10 93.99 Medvedev 11.65 12.83 16.25 15.85 9.50 6.01 %sub-category 30.62 21.08 6.65 8.68 14.42 0.74 4.85 12.97 100.00 73.52 Rus. Activists and Advocates Navalny 58.24 32.72 23.84 31.70 24.40 6.63 28.50 %sub-category 28.27 15.88 11.57 15.39 11.84 3.22 13.83 100.00 13.78 International Victims Shapps 8.10 7.58 19.00 %sub-category 23.36 21.86 54.79 100.00 2.32 %category 27.37 19.40 4.89 7.97 13.25 5.44 4.51 17.17 100.00 100.00 35.06 Location Ukrainian Locations Ukraine 185.68 64.14 24.38 47.69 23.78 4.70 7.58 123.60 74.71 Kyiv 11.65 12.83 47.50 11.17 Donetsk 46.42 12.83 9.50 10.67 Donbas 6.41 7.93 2.22 Luhansk 7.95 1.23 %sub-category 37.82 14.93 3.78 8.63 4.92 0.73 1.18 28.02 100.00 24.27 Ukrainian Nationality Ukrainian 46.42 6.41 8.13 15.89 15.85 24.40 38.00 %sub-category 29.93 4.13 5.24 10.25 10.22 15.73 24.50 100.00 5.84 Russian Locations Russia 382.96 44.18 17.66 222.55 31.15 28.35 113.63 275.80 78.59 Moscow 127.65 44.91 8.13 7.95 31.70 32.50 22.73 28.50 21.41 %sub-category 35.95 6.27 1.82 16.23 4.42 4.28 9.60 21.42 100.00 53.49 Russian Nationality Russian 127.65 6.41 8.13 7.93 8.10 7.58 47.50 100.00 %sub-category 59.85 3.01 3.81 3.72 3.80 3.55 22.27 100.00 8.03 Continued on next page (Back to Contents) 113 APPENDIX Table 55: Proper noun occurrences by years in the Leipzig corpora Category & Sub-category Prop. N. 2014 2015 2016 2017 2018 2019 2020 2023 %word %sub-cat. %cat. Disputed Areas Crimea 127.65 44.91 8.13 23.78 8.10 9.50 100.00 %sub-category 57.48 20.22 3.66 10.71 3.65 4.28 100.00 8.46 %category 39.77 9.15 2.81 11.37 5.35 4.00 5.71 21.84 100.00 100.00 62.26 Terms & Concepts UAV 6.41 7.95 7.58 21.94 PoWs 11.65 6.41 16.25 7.95 8.00 9.50 59.76 Neptune 6.41 8.13 14.54 %sub-cat. & cat. 12.11 19.98 25.33 16.52 8.31 7.88 9.87 100.00 100.00 2.26 Organizations Gazprom 11.65 6.41 100.00 %sub-cat. & cat. 64.51 35.49 100.00 100.00 0.42 %year 34.90 13.10 4.03 10.25 7.98 4.58 5.31 19.84 100.00 [Return to Section 5.1.2.3.1 Proper noun categories through time] 114 (Back to Contents) APPENDIX Figure 23: The occurrences of the Ukraine War proper noun categories in the Leipzig corpora Figure 23 illustrates the categories of the proper nouns summed up per year in the Leipzig corpora. The categories related to Russia and Ukraine (Nationality, Locations, Officials, and Activists and Advocates) are the most consistently identified, being present in almost every year with the absence of Ukrainian in 2020, Russian in 2017, and Navalny in 2016. Disputed Area (Crimea) is also discussed almost every year except in 2017 and 2020. Organizations and International Victims are the least frequently discussed with Gazprom being found only in 2014 and 2015, and Shapps only in 2019, 2020, and 2023. Last but not least, Terms and Concepts are detected every year. The overall trend in each category seems to be in a U shape where the proper nouns are mentioned most often in the year 2014 before the rate falls down sharply in the following years and surges up again towards 2023. This is in accordance with Russia’s annexation of the Crimean Peninsula in 2014, as well as the Russian invasion of Ukraine in 2023. One noticeable fluctuation is the spike in the middle during 2017 and 2018. The close-reading reveals a few possible causes, namely the NotPetya malware outbreak in June 2017 and the increasing diplomatic efforts involving Ukraine, Russia, and Western countries during that period. [Return to Section 5.1.2.3.1 Proper noun categories through time] (Back to Contents) 115 APPENDIX Table 56: Common pairs in the Ukraine War corpus and the 2014 and 2023 Leipzig corpora Common Pairs OF_Ukr EF_Ukr t_Ukr p_Ukr OF_14 EF_Lz14 t_Lz14 p_Lz14 OF_Lz23 EF_Lz23 t_Lpz23 p_Lpz23 people_killed 37 5.49 13.45 0.05 351 28.35 60.60 0.01 156 9.38 47.89 0.01 soldiers_killed 11 1.50 7.74 0.08 68 2.97 37.73 0.02 17 0.49 23.59 0.03 civilians_killed 10 1.80 6.10 0.10 43 1.26 37.19 0.02 19 0.34 32.24 0.02 people_injured 9 0.99 8.06 0.08 101 8.46 31.81 0.02 59 4.69 25.08 0.03 others_wounded 6 0.14 15.54 0.04 22 0.47 31.45 0.02 9 0.14 23.76 0.03 dozens_wounded 6 0.14 15.54 0.04 1 0.20 1.79 0.33 1 0.04 5.04 0.12 people_wounded 6 1.15 4.52 0.14 44 3.52 21.59 0.03 11 0.95 10.32 0.06 claim_verified 4 0.05 17.21 0.04 2 0.01 16.45 0.04 1 0.00 14.62 0.04 people_displaced 4 0.38 5.83 0.11 16 0.82 16.72 0.04 16 0.61 19.78 0.03 explosions_heard 3 0.02 22.20 0.03 10 0.03 55.00 0.01 2 0.02 15.70 0.04 election_held 3 0.06 12.31 0.05 49 1.66 36.70 0.02 14 0.90 13.78 0.05 explosions_reported 3 0.12 8.42 0.08 1 0.08 3.15 0.20 3 0.03 16.41 0.04 others_injured 2 0.12 5.38 0.12 12 1.13 10.24 0.06 30 0.69 35.35 0.02 schools_closed 1 0.00 18.18 0.03 25 0.37 40.63 0.02 9 0.30 15.75 0.04 what_happened 1 0.01 13.71 0.05 6 0.12 17.26 0.04 19 0.22 40.04 0.02 damage_done 1 0.01 12.82 0.05 34 0.45 49.84 0.01 17 0.32 29.67 0.02 woman_arrested 1 0.01 12.82 0.05 22 5.44 7.10 0.09 22 3.67 9.57 0.07 story_told 1 0.01 11.45 0.06 1 0.42 0.90 0.53 19 0.74 21.28 0.03 decision_made 1 0.01 9.64 0.07 124 4.92 53.69 0.01 111 6.02 42.78 0.01 what_described 1 0.02 7.83 0.08 22 1.40 17.39 0.04 31 1.81 21.69 0.03 efforts_made 1 0.02 7.83 0.08 9 1.11 7.48 0.08 21 1.75 14.54 0.04 decision_taken 1 0.03 5.91 0.11 22 4.82 7.82 0.08 42 3.97 19.07 0.03 bodies_found 1 0.03 5.32 0.12 75 3.90 36.02 0.02 27 1.68 19.56 0.03 what_left 1 0.04 5.02 0.13 22 3.28 10.34 0.06 38 5.34 14.14 0.04 man_taken 1 0.04 4.76 0.13 70 12.97 15.84 0.04 47 12.10 10.03 0.06 what_needed 1 0.04 4.67 0.13 32 3.49 15.25 0.04 31 3.47 14.78 0.04 people_arrested 1 0.44 0.85 0.55 82 20.93 13.35 0.05 56 12.71 12.14 0.05 Average 9.74 0.10 24.20 0.07 21.06 0.04 * OF = observed frequency, EF = expected frequency, t = t-score, p = p-value [Return to Overlapping pairs between the Ukraine War corpus and the Leipzig News corpora] 116 (Back to Contents) APPENDIX Topic 0: Score = 1724 school: 0.02628955032671426 student: 0.018996113417358643 teacher: 0.017585613102662018 education: 0.0172650371087878 college: 0.010945631308617343 university: 0.008745676366557315 class: 0.008725596022172289 grade: 0.008161995854324689 teach: 0.007477370054643667 scholarship: 0.00699409521631468 Topic 1: Score = 1447 gun: 0.025399912938399702 shooting: 0.021882691753651977 shoot: 0.020346913330146616 gunshot: 0.009902042988908373 shot: 0.009582867205230366 gunfire: 0.009459639047856691 officer: 0.008948877617724088 fatally: 0.00854381304840883 firearm: 0.008520968218965171 bullet: 0.008518714191217682 Topic 2: Score = 1201 fire: 0.043285775166403376 firefighter: 0.030869865327219984 flame: 0.015412655694815041 wildfire: 0.014450898128236446 blaze: 0.013519608828066723 burn: 0.013181841112312447 extinguish: 0.008568419687352372 engulf: 0.007443015343721647 firework: 0.006673054347745308 crew: 0.006521846701161178 Topic 3: Score = 1186 ukraine: 0.037800145168949986 russia: 0.028193685127463418 russian: 0.02741978510637296 ukrainian: 0.027019138718817764 putin: 0.02290869578492455 moscow: 0.015698758513487236 donetsk: 0.014202013468567741 minsk: 0.012878370915669141 vladimir: 0.012080941741008414 crimea: 0.01169367170310528 (Top 4 popular topics in the score report of the 2015 Leipzig corpus) Table 57: The transformation of topic keywords and scores Topic_Lpz_15 Score Norm. Sc. Keyword Education 1724 514.51 school, student, teacher, education, college, university, class, grade, teach, scholarship Gun Violence 1447 431.84 gun, shooting, shoot, gunshot, shot, gunfire, officer, fatally, firearm, bullet Firefighting 1201 358.43 fire, firefighter, flame, wildfire, blaze, burn, extinguish, engulf, firework, crew Ukraine Conflict 1186 353.95 ukraine, russia, russian, ukrainian, putin, moscow, donetsk, minsk, vladimir, crimea (This table shows how the topics from the score report are put in a tabular format.) [Return to Section 5.2.5 Topics in news reports] (Back to Contents) 117 APPENDIX Table 58: Topic keywords and scores (2014-2017) # Topic_Lpz_14 Score Topic_Lpz_15 Score Topic_Lpz_16 Score Topic_Lpz_17 Score 1Education 596.36 Education 514.51 Education & Schooling 1056.5 Music 550.21 2 Rus-Ukr Confl. [2] 555.67 Gun Violence 431.84 Parenting 607.89 Education 518.98 3Israel-Palestine Confl. 502.87 Fire & Firefighting 358.43 Legal & Court Matters 508.91 Film Industry 515.55 4Gun Violence 459.38 Rus-Ukr Confl. [4] 353.95 Religion & Clergy 420.07 Gun Violence 314.05 5Film Industry 387.95 Space Exploration 320.23 Fire & Firefighting 342.85 Medical Emergencies 278.19 6Medical Emergencies 372.42 Middle East Conflict 280.53 Music 310.65 Fire & Firefighting 260.67 7China Relations 361.23 Religion 228.90 Art & Museums 267.72 Parenting 245.43 8Music 299.73 Sexual Violence 225.02 Death & Obituaries 236.12 Weather & Nat. Dis. 186.35 9Fire & Firefighting 287.93 Parenting 211.00 Sports & Games 233.14 Obituaries 184.05 10 Space Exploration 230.16 China Relations 197.27 Journalism & Publishing 218.83 Art 183.48 11 Parenting 227.36 Greek Crisis 187.12 Cooking & Food 210.48 Gender Issues 175.37 12 Weather & Nat. Dis. 201.58 Israel-Palestine Confl. 182.94 Weather & Nat. Dis. 193.19 Pets 165.63 13 Sexual Violence 184.81 Paris Attacks 176.38 Film Industry 183.65 Culinary 159.64 14 Soccer 184.19 Crime 169.51 Medical Emergencies 170.23 Prison 150.06 15 Pets 182.33 Cybersecurity 153.40 Charity & Volunteering 166.65 Israel-Palestine Confl. 99.08 16 Ebola Outbreak 182.01 Weather & Nat. Dis. 148.03 Funerals & Memorial 165.76 Rus-Ukr Confl. [30] 97.89 17 Smartphones 174.25 Music 146.53 Fashion & Clothing 159.50 18 Protests 166.48 Film Industry 45.06 Fishing & Wildlife 156.82 19 Alcohol & Beverages 155.92 20 Pets & Animals 151.45 21 Agriculture & Farming 144.89 22 Israel-Palestine Conflict 126.71 23 Rus-Ukr Confl. [74] 48.89 24 Gun Violence 18.48 *Rus-Ukr Confl. = Russia-Ukraine Conflict [Return to Section 5.2.5 Topics in news reports] 118 (Back to Contents) APPENDIX Table 59: Topic keywords and scores (2018-2023) # Topic_Lpz_18 Score Topic_Lpz_19 Score Topic_Lpz_20 Score Topic_Lpz_23 Score 1Weather & Nat. Dis. 653.67 American Football 719.87 US Election 1752.5 Music 538.85 2Food & Dining 642.25 Music 623.77 Education 794.35 Education 464.69 3Music 599.72 Education 584.69 Sanity Measures 448.28 Israel & Palestine 458.70 4Education 535.21 Cryptocurrency 405.81 Gun Violence 371.56 Sports (Soccer) 338.89 5Soccer & Sports 405.62 Housing Market 346.47 Music 335.11 Crime 290.39 6American Football 344.25 Social Media 303.92 Fire & Firefighting 320.04 Food & Beverage 242.19 7Fire & Firefighting 343.39 Fire & Firefighting 291.19 Coronavirus 289.90 Fire & Firefighting 231.63 8Parenting 305.14 Transportation 287.72 Weather & Nat. Dis. 267.16 Water & Weather 230.20 9Israel-Palestine Conflict 287.16 Culinary Arts 277.01 Court 264.42 Economic Growth 229.06 10 Elections & Voting 269.46 Brexit 264.85 Parenting 218.93 Weather Conditions 204.53 11 Religion & Clergy 260.61 Parenting 260.51 Quarantine 202.22 Pets & Animals 195.69 12 Nigerian Politics 245.77 Medical Emergencies 190.75 Middle East Conflict 197.56 Events & Festivals 193.69 13 Energy & Resources 223.50 Police Investigations 181.20 Fashion 180.85 Rus-Ukr Confl. [13] 170.30 14 Rus-Ukr Confl. [14] 190.11 Obituaries 174.83 Vaccine 179.48 NFL Football 168.02 15 Film Industry 172.98 Pets 157.17 Religion 171.80 Drug-related Activities 164.31 16 Prisons & Sentencing 163.85 Religion 153.99 Pets 169.61 Maritime Activities 159.17 17 Drugs & Addiction 149.86 Middle East Conflict 148.49 Soccer 167.97 Art & Museums 154.33 18 Pets & Animals 146.43 Weather & Nat. Dis. 127.36 Virus 155.64 Religion & Church 153.47 19 China & Trade 143.58 Gun Violence 100.44 Gaming 155.36 Movies & Cinema 149.76 20 Gun Violence 88.20 Israel-Palestine Confl. 88.57 Cryptocurrency 154.54 Fashion & Clothing 146.05 21 Film Industry 71.78 Traffic 151.80 Parenting 141.77 22 Rus-Ukr Confl. [88] 57.31 Lockdown 150.43 23 American Football 150.16 24 Racism 146.32 25 Israel-Palestine Confl. 101.38 26 Film Industry 70.97 27 Rus-Ukr Confl. [188] 19.18 *Rus-Ukr Confl. = Russia-Ukraine Conflict [Return to Section 5.2.5 Topics in news reports] (Back to Contents) 119 APPENDIX Figure 24: Semantic categories of agentive by-phrases in the Ukraine War corpus [Return to Section 5.3.1 By-agent semantic categories] 120 (Back to Contents) APPENDIX Table 60: Anglo-Saxon and non Anglo-Saxon media Website Frequency Anglo-Saxon Media Country Explanation aljazeera.com 11 No Qatar Al Jazeera is a Qatari news network, not Anglo-Saxon. en.wikipedia.org 6 No Wikipedia is an online encyclopedia. iiss.org 3 No UK IISS (International Institute for Strategic Studies) is a think tank. nejm.org 3 No USA NEJM (New England Journal of Medicine) is a medical journal. nato.int 2 No NATO (North Atlantic Treaty Organization) is a political-military alliance. theconversation.com 1 No The Conversation is an independent academic source. dw.com 1 No Germany Deutsche Welle (DW) is a German public international broadcaster. warontherocks.com 1 No War on the Rocks is a platform for analysis and debate. iea.org 1 No UK The Institute of Education (IE) is a research institute. Non Anglo-Saxon Media 29 (21.01%) cnn.com 18 Yes USA CNN is an American news network. nytimes.com 16 Yes USA The New York Times is an American newspaper. bbc.com 13 Yes UK BBC is a British public service broadcaster. theguardian.com 10 Yes UK The Guardian is a British newspaper. apnews.com 9 Yes USA Associated Press is an American news agency. vox.com 7 Yes USA Vox is an American news and opinion website. cnbc.com 5 Yes USA CNBC is an American business news channel. reuters.com 5 Yes UK Reuters is a UK-based global news agency. news.sky.com 4 Yes UK Sky News is a British news channel. bloomberg.com 4 Yes USA Bloomberg is an American global financial services provider. rand.org 4 Yes USA RAND Corporation is an American research institution. npr.org 2 Yes USA National Public Radio is an American non-profit media organization. washingtonpost.com 2 Yes USA The Washington Post is an American newspaper. foreignpolicy.com 2 Yes USA Foreign Policy is an American magazine. ft.com 2 Yes The Financial Times is a British international business newspaper. nbcnews.com 1 Yes USA NBC News is an American news network. atlanticcouncil.org 1 Yes USA Atlantic Council is an American think tank. cfr.org 1 Yes USA Council on Foreign Relations is an American think tank. irishtimes.com 1 Yes Ireland The Irish Times is an Irish newspaper. 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