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«ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ» В МЕДИЙНОЙ КОММУНИКАЦИИ -
DOI: https:// doi. org/10.15688/jvolsu2.2024.5.9
UDC 811.161.1'42 Submitted: 26.01.2024
LBC 81.411.2-51 Accepted: 24.06.2024
MEDIATIZATION OF ARTIFICIAL INTELLIGENCE CONCEPT IN THE RUSSIAN LANGUAGE MEDIA DISCOURSE: CORPUS-BASED APPROACH1
Elena Yu. Ilyinova
Volgograd State University, Volgograd, Russia
Larisa A. Kochetova
Volgograd State University, Volgograd, Russia
Abstract. Based on corpus-assisted discourse analysis, the paper studies the mediatization of artificial intelligence (AI) technology in the Russian-language media discourse as a construe technique that shapes AI perception and evaluation as a concept of new social reality. The paper reveals linguistic portraying of the AI concept in Russian digital media corpus, construed by business-oriented outlets Kommersant, Vedomosti, RBC, and popular media resources, such as Lenta.ru, Argumenty i Fakty, Komsomolskaya Pravda. Corpus-assisted discourse analysis comprises aims to extract the quantitative parameters of texts and establish their correlations with content parameters; define the themes of narratives about AI, which determine its discursive interpretation, and describe their distribution across the Russian-language news digital corpus; define discursive strategies used for designing the image of AI. The quantitative characteristics of the texts construing AI imagery in the corpus under study point to the prevalence of small and moderate size texts, which is explained by the pragmatics of informing a broad lay audience on advancement and development of AI without initiating any public discussions. The thematic distribution analysis showed domination of "Positive AI capabilities" and "AI development and Investments", whereas "Impending danger" and "Negative AI capabilities" are covered infrequently. Argumentation in the explanatory and prognostic strategies introduces the topoi of inevitability, necessity, and rivalry in AI ^ development. The explanatory strategy expands on the nature of AI, its functions and potential. The prognostic o strategy delivers information on the development and advancement of AI technology, portraying efficiency scenarios, 2 but only a tiny fraction of the texts warn about the negative consequences of AI. The novelty of the results lies in j- the establishment of contradictory mediatization of the AI concept, which, on the one hand, is aimed at depicting g its positive portrayal and capability of bringing social and economic benefits. On the other hand, it contains a ■§ warning about its potential dangers and risks if the spheres of its application expand.
is Key words: medialinguistics, artificial intelligence, mediatization, topic representation, discursive strategy, W corpus-assisted discourse analysis, key words analysis.
Citation. Ilyinova E.Yu., Kochetova L.A. Mediatization of Artificial Intelligence Concept in the Russian ® Language Media Discourse: Corpus-Based Approach. Vestnik Volgogradskogo gosudarstvennogo universiteta. § Seriya 2. Yazykoznanie [Science Journal of Volgograd State University. Linguistics], 2024, vol. 23, no. 5, pp. 108-123. DOI: https://doi.org/10.15688/jvolsu2.2024.5.9
©
УДК 811.161.142 ББК 81.411.2-51
Дата поступления статьи: 26.01.2024 Дата принятия статьи: 24.06.2024
МЕДИАТИЗАЦИЯ КОНЦЕПТА «ИСКУССТВЕННЫМ ИНТЕЛЛЕКТ» В РУССКОЯЗЫЧНЫХ СМИ: КОРПУСНЫЙ ПОДХОД1
i
Елена Юрьевна Ильинова
Волгоградский государственный университет, г. Волгоград, Россия
Лариса Анатольевна Кочетова
Волгоградский государственный университет, г. Волгоград, Россия
Аннотация. Методом дискурсивно-корпусного анализа изучена русскоязычная практика медиатизации технологии искусственного интеллекта (ИИ), формирующая его восприятие и оценку как концепта новой социальной реальности. Материалом исследования послужили тексты бизнес-ориентированных изданий «Коммерсант», «Ведомости», «РБК», а также популярных медиаресурсов «Lenta.ru», «Аргументы и факты», «Комсомольская правда». Дискурсивно-корпусный анализ включает: извлечение количественных параметров текстов об ИИ и установление их корреляции с содержательными параметрами; определение тематики нарративов об ИИ, задающей специфику его дискурсивной интерпретации, описание дистрибуции тем в собственном русскоязычном цифровом корпусе новостных текстов; выделение дискурсивных стратегий конструирования образа ИИ. Анализ количественных параметров продемонстрировал доминирование текстов малого и среднего размера, что объясняется прагматикой информирования общественности о достижениях и перспективах развития в области ИИ: они не предназначены для стимулирования публичных дискуссий. Тематика нарративов об ИИ представлена в десяти категориях при доминировании тем «Положительные характеристики ИИ» и «Развитие и финансирование технологий ИИ», темы «Угрозы, связанные с ИИ» и «Негативные следствия ИИ» не являются активно востребованным. В аргументации используются объяснительная и прогностическая дискурсивные стратегии, основанные на топосах неизбежности, необходимости, конкуренции в области ИИ. Тексты, реализующие объяснительную стратегию, характеризуют природу ИИ, его основные его функции и возможности. Прогностические тексты, передающие информацию о развитии и совершенствовании технологий ИИ, преимущественно рисуют положительные сценарии будущего, малая часть текстов содержит предостережения о возможных негативных последствиях ИИ. Новизна полученных результатов заключается в объективизации данных о стратегиях медиатизации феномена ИИ, которые направлены на конструирование положительного образа ИИ, возможности решения с его помощью социальных и экономических проблем и включают предупреждение о потенциальных опасностях и рисках при расширении сфер применения ИИ.
Ключевые слова: медиалингвистика, искусственный интеллект, медиатизация, тематическая репрезентация, дискурсивная стратегия, корпусный анализ дискурса, метод ключевых слов.
Цитирование. Ильинова Е. Ю., Кочетова Л. А. Медиатизация концепта «искусственный интеллект» в русскоязычных СМИ: корпусный подход // Вестник Волгоградского государственного университета. Серия 2, Языкознание. - 2024. - Т. 23, №№ 5. - С. 108-123. - (На англ. яз.). - DOI: https://doi.org/10.15688/^оки2.2024.5.9
The phenomenon of Artificial Intelligence (AI) has recently aroused sensationally strong curiosity and provoked heated discussions of technologically advanced crews and a general community thus shaping paradoxically equivocal public perception of AI, either as a great technological invention that promises an incredible potential for tackling social challenges or as an imminent and inescapable danger for humanity.
Introduction
Initially, AI was associated with the process of engineering and making intelligent machines or computer programs that might explore intelligence as the computational part of the capability to store and analyze big data and so solve some urgent social challenges [McCarthy, 2007]. Having gained certain technological advancement by demonstrating far-reaching and profound impact on various spheres of modern social life [Elias, 2023], AI has undoubtedly sparked common interest to its branches and applications and has become a prominent topic in
current discussions. Despite its positive appreciation, AI is being portrayed by media as a 'double-edged sword' whose risks include misuse, errors and the possibility of malfunctions resulting in severe consequences, as well as privacy concerns, ethical dilemmas [Cheatham, Javanmardian, Samandari, 2019; Nguyen, Hekman, 2022]. The information basis limitations are also being debated as lacking a sense of 'truth' that results in its false answers [OpenAI, 2023]. Increasing prominence in the public eye and paradoxically alternative appreciation of AI as a new socially important concept calls for linguistic monitoring on how online and print news media contribute to its discursive portraying for the public through certain media techniques. Such studies should be based on methodology of medialinguistics [Dobrosklonskaya, 2020], which sets as its goal examining the process of mediatization of various concepts and phenomena of social importance [Klushina, 2014; Shmeleva, 2015]. Mediatization as a phenomenon under study is viewed as a discursive model capable of enduring impact on community consciousness by special tactics of informing the public about technologically or socially vital concepts, phenomena, practices or trends [Ilyinova, Volkova, 2023]. In this regard, media do not only verbalize concepts and ideas, giving them an evaluative interpretation, but also construe and introduce images able to manage the perception or the angle of the public evaluation.
Depending on the choice of focus, mediatization might be described in philosophical, institutional, cultural-and-social perspectives, or studied as discursive practice in communications. The institutional approach considers it as a social process, which is stimulated both by the development of media and by the dynamics of other institutions, in which social agents try to use media resources to attain their purposes [Hjarvard, 2013, pp. 123]. The socio-constructivist approach aims at the study of coordination between the media channels improvement and cultural changes in everyday communicative practices [Lundby, 2014, pp. 10-11], or on how such alternations in practices are connected with the change of communicative construction of reality [Hepp, Couldry, 2013, pp. 118].
Meditatization is viewed in Russian medialinguistics as the cognitive-and-discursive process of impelling the individuals' or public consciousness by various media techniques,
consequently, a mental picture of the world is formed through specific media cogniostrategies [Rogozina, 2003, pp. 124]. Presumably, the process of qualitative changes in public consciousness goes under the influence of media information methods [Kozharnovich, 2021, pp. 423], and media is described as a special communicative channel with the aim to stir zones of public discussion on socially urgent issues [Klushina, 2014]. Mediatization encompasses various spheres of social world, therefore, the objects of meditatization may be urgent concepts in politics [Baykulova, 2017], jurisprudence and law [Annenkova, Chernogor, Pilgun, 2018; Kondratyeva, Ignatova, 2021], professional discursive practices in journalisms, news making on technologies and social phenomena [Bednarek, Caple, 2012]. It is worth noting that the media interpretation of socially important knowledge contributes not only to popularization of reliable and proficient information, thus improving the quality of the content, but also bears the risk of generating and disseminating destructive social practices [Kozharnovich, 2021; Kochetova, Ilyinova, 2023].
All of the above points to cognitive complexity of the methodology sufficient for discursive study on mediatization of any contemporary or newfangled concept of the day. However, it is due to corpus linguistics techniques that the researchers can provide objective judgments on the media discursive practices, enabling detection of the trends with the quantitative data. We assume that corpus tools, based on an array of texts, help establish common themes, patterns and strategies of conceptualizing socially significant phenomena, shaping their perception by members of the discursive community through deriving value meanings.
As the scientific inventions and technologies are gaining their popularity these days, media discourse gains control over determining their social assessment by multiple descriptions in various styles of information delivery. Talking on undergoing actuality changes, mass media as a powerful tool of mediatization have focused public attention on such scientific and technological developments as nanotechnology [Cutcliffe, Pense, Zvalaren, 2012], cloning [Holliman, 2004], gene modification [Tucker, 2013], and digital technology [Guzman, Jones, 2014], thus playing a crucial role in commenting on these concepts.
As far as artificial intelligence technologies, whose rapid spread has sparked debate about their benefits, potentials and risks, have become the focus of media agenda, it deserves setting a corpus-discursive monitoring on an estimative perception of AI, the Big Data concepts and neural networks in modern media. We suggest it that news reports make such trends visible. By discursive representations of benefits, errors and risks, they shape the perception of AI technologies, thus influencing public appraisal [Lupton, 2017; Pentzold, Landinez, Baaken, 2019; Zhai et al., 2020].
Recent discursive studies have identified various tints of AI perception, depicting it as an instrument of social control and (or) development [Kolyanov, 2022]. Other studies on AI technologies in media are limited to selected countries, such as the US [Fast, Horvitz, 2017; Garvey, Maskal, 2020], the UK [Roe, Perkins, 2023; Kochetova, 2023]. Several Russian researchers have explored professional discourses that depict the ideologeme of AI as a phenomenon regulated by competent bodies and empowering businesses with its beneficial potential, along with amplifying the capabilities of human beings [Sokolova, 2023]. As a result, modern academic descriptions of AI are focused on its integration into cognitive net of mass discourse, thus indicating its crucial role in shaping present and future sociotechnical pathways. However, representations of AI in Russian public media discourse and the vectors of its social assessment through the discursive techniques and linguistic resources of mediatization in various news outlets have not yet been deeply investigated.
This study is aimed at discursive analysis of narrative topics of artificial intelligence in Russian-language digital news outlets over 2020-2023. Based on a corpus-assisted discourse analysis of the most popular news websites, it seeks to assess how the media portrays AI to Russian public and discover direct and implicative strategies of its mediatization. The research questions are as follows: 1. What quantitative characteristics do the narratives about AI demonstrate in terms of the text size and lexical variety and do they differ when they exploit a specific topic? 2. What topics related to AI do the Russian media promote and how are they distributed across the corpus? 3. What strategies for discursive portraying of AI are employed in the Russian quality and popular digital media sources? What rhetoric topoi do they rely on?
Material and methods
The research is based on the constructivist theory of discourse, viewing it as a set of texts that define the social and political context of a phenomenon, forming ideology or way of thinking [Laclau, 1995]; understanding discourse as a substantive and thematic commonality of texts [Chernyavskaya, 2017, p. 142]; theory of mediatization [Klushina, 2014]; corpus-assisted discourse studies [Baker, 2006; Teubert et al., 2007; Anashkina, Konkova, 2021]; analysis of modern Russian media discursive practices [Brodovskaya et al, 2019; Zmazneva, 2018]; research tackling linguistic representation of socially significant phenomena [Efremova, 2017; Rebrina, 2021; Kondratyeva, Ignatova, 2021; Parvaresh, 2023; Sokolova, 2023].
With a growing interest in mediatization techniques, automatic text analysis tools are of great importance as they enable the processing of a bulk of texts related to phenomena under study (see, e.g. [Boykoff, Roberts, 2007; Grundmann, Scott 2014; Schmidt, Ivanova, Schäfer, 2013]). The use of corpus linguistics (CL) methods, including frequency lists, keywords, clusters, collocates, and concordances, is crucial to corpus-assisted discourse analysis [Baker, 2006; Liu, 2022]. On the one hand, computerassisted corpus analysis opens the gate to large text samples examination and important linguistic patterns identification; on the other, the corpus-analysis findings must be interpreted and described by integrating discourse analytic methods and theories [Baker, McEnery, 2015; Baker et al., 2008; Cheng, 2013; Liu, Ma, 2022]. In discourse analysis, CL methods allow dealing with large numbers of texts, making empirical test research assumptions and generative findings which would be impossible through manual analysis of a small sample, thus reducing researcher bias in selecting and interpreting data, and replicating analytic procedures [Baker, McEnery, 2015; Friginal, Hardy, 2014; Lin, 2021].
The corpus compiled for this study comprises narratives about artificial intelligence (AI), selected by keywords and phrases in search queries. Core units in the list include iskusstvennyj intellekt (artificial intelligence), nejroset' (neuro network), roboty (robots), robototehnika (robotics), chat GPT, Yandex-GPT.
The samples were retrieved from the news media outlets that were selected as appealing to professionals at Kommersant, Vedomosti, RBC, and general audience at Argumenty i Fakty, Lenta.ru, Komsomolskaya Pravda with the three criteria: circulation, content and target audience. The first three media outlets have a clear bias towards business news and target a business-oriented audience concerned for analytics, economy and business. More than 9 mln unique visitors search the website www. vedomosti.ru every month, www.kommersant.ru reports on about 26 mln visitors a month. According to https:// companies.rbc.ru/statistics/, the website of the RBC daily newspaper registers over 2 mln users every month. The other group includes popular media outlets. In 2021-2022, the site of Komsomolskaya Pravda had about 80-95 mln unique visitors a month. Every weekly issue of AiF.ru is reported to be read by 6 mln people. Lenta.ru stays among frequently visited digital news sites with about 22 mln visits a month (https://www.similarweb.com/ru/ website/lenta.ru/#traffic). These media are thought to be influential voices in Russian media space and they cover artificial intelligence from different angles. Table 1 presents data on the structure of the media texts corpus "Artificial Intelligence" (AIC). The corpus comprised 243 documents, the corpus size is 298 157 tokens.
The compiled corpus was processed with LancsBoxX (version 3.0.0.) [https://cqpweb. lancs.ac.uk/], offering the function of downloading a corpus built using one's own data. The analysis of the lexical material of AIC was carried out with the methods of corpus linguistics, including the concordance and contextual analysis, techniques of semantic analysis, statistics, and interpretation of discursive semantics. The research design included several steps. We used the corpus tools to retrieve the information about the quantitative characteristics of texts, such as size and lexical density. Then, we sought to identify topics of the narratives that position the technology in certain contexts and give it the
prominence by emphasizing specific features. For this purpose, we first read all the texts in the corpus closely, identifying core issues and themes in the depiction of AI and establishing their distribution across the corpus. Secondly, we identified discursive strategies of AI representation. We also revealed language means used to construe rhetorical topoi, such as inevitability, necessity, and leadership of AI across media outlets, independent of the resulting imaginations. We explored the relationship between these building blocks, understanding them as part of a coherent (but not necessarily linear) narrative leading to a specific AI vision. Finally, we examined the pragmatics of the texts in order to uncover their leading intentions, and we established correlation between the text size and its pragmatic functions, which allowed us to draw some inferences.
Results and discussion
Quantitative characteristics
of the texts construing AI imagery in the Russian news media discourse
One of the oldest problems in research concerning discourse that is meant to inform the addressee is how to determine whether or not a discourse is comprehensible to a specific target group. Among the quantitative parameters that are used to measure the comprehensibility of the texts researchers enumerate lexical density, text size and text complexity. Lexical density is defined as the proportion of the number of word types to the number of tokens. As pointed out in [McCarthy, Jarvis, 2010], lexical diversity can be assessed in many ways and each approach may be informative as to the construct under investigation. For the comparison of files of varying sizes, we need to go beyond a simple Type/token ratio (TTR), which expresses the proportion of types relative to the proportion of tokens, and compute more sophisticated measures such as Standardized type/token ration (STTR) or Moving average type/token
Broadsheet papers The number of words An average number of words The number of texts Tabloid papers Number of words An average number of words The number of texts
vedomosti.ru 31004 620 50 lenta.ru 13923 278 50
kommersant.ru 39502 564 70 Aif.ru 10078 373 27
rbc.ru 6683 445 15 Kp.ru 17144 816 21
Total 77189 543 135 Total 41145 489 108
Table 1. The structure of the Russian language "Artificial Intelligence" Corpus (AIC) (2021-2023)
ratio (MATTR). TTR cannot be used to compare texts of different sizes in a corpus because it is calculated by dividing the number of types in a text or corpus by the number of tokens, so it decreases with text size. Moving average type/token ration (MATTR) is calculated by dividing a text into standard sized overlapping segments as a window moves through the file one token at a time. TTR is calculated for each overlapping segment and then the mean value of the TTRs is taken. MTTR is suitable for comparing texts of different sizes. As the size of texts is mainly within the range between 99 and 300, we chose the size of the window to be equal to 50 words (MATTR50). The non-standardized type/token ratio of the whole corpus is 0.1701, the STTR1000 is 0.5548, whereas MTTR50 values calculated for each text are much higher and vary from 0.83 to 0.96. These findings allow us to conclude that in general AI narratives employ a limited vocabulary that can be reduced to a restricted number of topics. However, high values for individual texts can be explained by the high number of word types in Russian due to the use of inflections, and the low frequency of grammar words within the boundaries of a single text.
The Text tool that is available in LancsBoxX (version3.0.0) provides an overview of all files
(texts) in the corpus, their size and lexical diversity. It also allows in-depth analysis of individual texts in the full view mode. The tool also searches texts and offer an overview table with a breakdown of frequencies and relative frequencies per file. It can be used for the purpose of visualization of corpus files and understanding their distribution in terms of their sizes, lexical diversity and frequencies of linguistic features in them presented in Figure.
As the data in Table 2 show, a text size varies from 99 to 3000 words. The texts were divided into groups that fall within the following intervals: 99-300 words, 301-600 words, 6011000 words, 1001-1300 words, 1301-2000 words, more than 2000 words.
Most texts, more than a half of the total number (53.4%), lie within the range from to 99 to 300, regardless of the type of the media outlets. With an increase in the number of words the number of texts dropped dramatically, with only four texts being of a significant length, which requires a reading of more than 8 minutes. These figures have important implications. It means that the texts about AI as well as other texts on web sites devoted to digital technologies are not supposed for long reading and their function is mainly to inform the reader about advancements in the field and
The distribution of texts according to the size in the AI media corpus
Table 2. The texts ranked according to the size in the corpus
The size range The number of texts %
99-300 129 53.4
301-600 62 25.5
601-1000 19 7.8
1001-1300 15 6.1
1301-2000 14 5.7
more than 2000 4 1.5
Total 243 100
prospects of its use. They are aimed to build awareness of the topic but not to stimulate discussions. Long texts employ an explanatory strategy that introduces the concept of AI to a general audience, which aims to overcome potential distrust and fear towards the new technology. The strategies employed to construe AI imageries and correlations between a text size and its topic will be discussed in the next sections.
Qualitative characteristics of the texts construing AI imagery in the Russian news media discourse
Topic modelling across the media segments in the AI corpus
To model topics, we followed the categories developed in [Roe, Perkins, 2023], who identified six themes as 'impending danger', 'explanation/ information', 'negative capabilities of artificial intelligence or ChatGPT', 'positive capabilities of
artificial intelligence or ChatGPT', 'Humorous/ Comedie', and 'Experimental Reporting'. Based on the content of our corpus, we modified the themes and identified the ten most frequent ones that were labelled as 'Employment', 'Impending danger', 'Positive AI capabilities', 'Negative AI Capabilities', 'Legal Regulation', 'Education', 'Events', 'Economic Benefits', 'AI development and investment', and 'Experiments with AI'. The themes are listed in Table 3 with a brief description highlighting some of the key linguistic features that characterize them.
The content analysis revealed that the most common theme in the articles was 'Positive Capabilities of AI' that describes how AI can benefit individuals or society. Different news outlets give varying prominence to the application of AI in assorted spheres, including industry, medicine, banking, legal systems, agriculture, management, and governance, as can follow. In example (1), AI is described as having the potential to reduce time and effort for people applying to the bank for a mortgage:
Table 3. Themes identified across the Russian language media outlets
Theme Description
Positive AI capabilities Articles describe ways in which AI can generate overall benefit for society or improve upon an existing, human-driven process
Impending danger Articles used highly evaluative language indicating imminent societal damage, disruption, or severe undesirable consequences relating to unforeseen impact of AI (e.g. climate change, fake news, plagiarism etc.)
Employment Articles emphasize the shortage of qualified staff for AI industry
Negative AI capabilities Articles describe what could be employed with ChatGPT or AI in order to commit crimes, esp. in cyberspace or harm others
Legal regulation Articles used modal words to state the necessity of legal regulation of AI, exercise control over its development and application
Science and Education Articles describe the use of AI in education, the issue of using GPTchats in searching for information for research and students' papers
Events Articles describe meetings, panel discussions, exhibitions, conferences etc. devoted to AI development and implementation
Economic benefits Articles describe benefits and profits that can be generated due to the use of AI
AI development and investment Articles report on advancements in AI development, specify the amounts invested in the field, explain the basic functions of artificially intelligent programs and chatbots, including YandexGPT, or describing a recent product addition or upgrade
Experimental use of AI Articles report on individual uses AI or YandexGPT in an experimental way to complete tasks, write a text, or achieve an outcome in an experimental fashion
(1) Альфа-Банк научил искусственный интеллект одобрять ипотеку без анкеты (Kommersant).
Text (2) demonstrates applications of AI in medical routines where it can replace a human General Practitioner (doctor) and carry out the task in a better manner:
(2) Меньше рутины, больше дела: как нейро-сети помогают московским врачам (rbc.ru)
They both give a use-case of AI which shows a positive impact that mostly concerns the change in the routine procedures.
The category 'Impending danger', which is in contrast with the 'Positive capabilities of AI' category, includes articles that discuss potential disruptions and dangers posed by AI technologies. Articles like these tend to emphasize the unforeseen impacts of AI technologies that may cause societal damage or severe consequences, such as replacing humans in various spheres, possessing abilities that surpass humans, spreading propaganda on a global scale etc., e.g.:
(3) Опасность «слишком развитого ИИ» заключается в том, что он может наводнить каналы пропагандой, отнять у людей работу и привести к созданию нечеловеческого разума, который способен перехитрить человека и сделать его знания устаревшими и ненужными.
The theme 'Employment' raises issues related to the shortage of qualified workforce in AI industry. If an article mentions the loss of jobs due to AI development we include it into the category of 'impending danger', e.g.:
(4) Представители офисных профессий могут в будущем потерять работу из-за искусственного интеллекта (ИИ) (lenta.ru).
By contrast, the theme 'Employment' stresses a low competition in the AI industry, which contributes to the promotion of university programs related to AI development and training, e.g.:
(5) По данным hh.ru, конкуренция на вакансии, связанные с работой в этих областях, очень низкая: менее двух человек на место (kommersant.ru);
(6) Искусственный интеллект ищет добрые руки. Главным ограничением его внедрения становятся кадры (kommersant.ru).
The topic 'Education' generally describes consequences for schools and universities arising from AI implementation, in (7) the article reports that universities are ready to embrace AI technologies and allow students to use GPT or chat-bots to prepare graduate papers; in (8) the article emphasizes the necessity of transformations and changes in education due to AI development, text (9) describes the use of AI in education as inevitable:
(7) В МГПУ разрешили использовать искусственный интеллект для написания дипломов (lenta.ru);
(8) Как ИИ меняет высшее образование. Развитие технологии искусственного интеллекта требует изменений от системы высшего образования. Трансформировать нужно не только программы обучений, но и сами методики преподавания и контроля (rbc.ru);
(9) Участники ПМЭФа оценили угрозу искусственного интеллекта для высшего образования. В Минобрнауки считают борьбу с его применением бессмысленной (lenta.ru).
We identify the topic 'Events', when articles inform about conferences, forums, meetings, panel discussions devoted to public talks about AI achievements and development, future scenarios of its implementation, e.g.:
(10) Сбер рассказал о перспективах искусственного интеллекта в креативных индустриях. <...> Сессия прошла в рамках фестиваля-форума «Российская креативная неделя». На ней выступили спикеры Сбера - Денис Димитров, исполнительный директор по исследованию данных Управления перспективных технологий AI и Екатерина Дятко, исполнительный директор Департамента данных и рекомендательных систем В2С (lenta.ru).
The topic 'Legal regulation' discusses issues related to copyright, regulation and control over the AI development, e.g.:
(11) В правительстве обсуждают новое регулирование работы с нейросетями (kommersant.ru).
The two related topics are 'Economic benefits' and 'AI development and investment'. The former raises the issues of positive changes in the economy, describe benefits and profits that can be generated due to the use of AI, e.g.:
(12) Российские ученые нашли способ ускорить обучение искусственного интеллекта (lenta.ru);
(13) Искусственный интеллект обойдется России дороже (vedomosti.ru);
(14) Исследовательские центры запросили дополнительное финансирование для проектов в области ИИ (vedomosti.ru).
The latter reports on advancements in AI development, specify the amounts invested in the field, explain the basic functions of artificially intelligent programs and chatbots, including YandexGPT, or describing a recent product addition or upgrade, e.g.:
(15) ...«Яндекс» запустил бета-версию нейро-сети для генерации изображений по текстовым запросам пользователей. Его назвали «Шедеврум». Приложение доступно на мобильных платформах Android и iOS (vedomosti.ru).
Articles report on individual uses AI or YandexGPT in an experimental way to complete tasks, write a text, or achieve an outcome in an experimental fashion, which often use the verbs запустить (launch), испытать (test), e.g.:
(16) Медиатехнолог Лобушкин запустил интервью-шоу с участием ИИ (lenta.ru).
The comparative analysis of Russian-language narratives about AI from the two types of media outlets showed that they raise similar topics, however, there are some differences in their distribution across the media segments (Table 4).
We compared the number of the texts and the word count for each AI topic to see which AI issues are at the top of promotion
The analysis of the AI Russian Corpus (AIC) show that in both outlets categories the two dominants themes are ' Positive AI capabilities', which accounted nearly half of the total number of articles collected (44.8 and 49.3%); 'AI development and Investments' comprise about a quarter of the texts in the popular press (26.1%) and only 15% in the business-oriented outlets.
The analysis of concordances revealed that the theme 'Positive AI capabilities' seems to vary significantly with the type of the newspaper outlet. The AIC shows that business-oriented outlets, such as Kommersant, emphasize AI capabilities in banking, industry and agriculture, thus, relating its implementation to societal values; popular press raises more often AI positive capabilities in medicine (20%), which accounts for almost two times higher texts than in the quality segment (10% vs 20%, respectively). We may suggest that the priority of this topic in the popular press is due to the target audience it addresses. Health issues are of interest to a broad lay audience, they are oriented towards individual values and echo with numerous health-related TV programs and shows. AI products are often portrayed as a relevant and competent solution to a range of public problems. Journalists or commentators rarely question whether AI-containing technologies are the best solutions to such problems or acknowledge ongoing debates concerning AI potential effects.
AI topics Business Press Po pular Press
Number Number % Number Number %
of texts of words of texts of words
Positive AI capabilities 61 20 021 4.8 46 13 116 49.5
AI development and 24 18 973 15.3 28 10 927 26.1
Investments
Impending danger 0 0 G 10 8 067 9.3
Experiments 6 3 933 4.4 5 879 4.6
Negative AI capabilities 7 7 767 5.1 4 1 206 3.7
Education 3 1 276 2.2 4 2 565 3.7
Employment 5 2 827 3.6 3 2 586 2,8
Economic benefits 8 5 761 5.8 3 2 231 2.8
Events 5 5 546 3.6 2 733 1.8
Legal regulation 7 4 026 5.1 - - -
Unclassified 10 10 601 7.3 2 706 5.6
Total 136 80 731 - 107 39 853 -
Table 4. Artificial Intelligence topics distribution in the Russian language media
The next dominant topic across the media is 'AI development and Investments'. The theme recurred frequently in the two types of the media, however, AI development receives twice as much attention in the popular press than in the business press, which means that it mostly focuses on the future scenarios of its implementation while the business-oriented outlets concentrate on the present achievements:
(17) «Сбер» представил нейросеть GigaChat. Она умеет отвечать на вопросы пользователей, поддерживать диалог, писать программный код. В отличие от ChatGPT нейросеть генерирует не только текст, но и картинки, а также более грамотно общается на русском языке (rbc.ru).
The texts are often associated with specific products or corporations and often mention companies involved in AI development, such as Sber, MTC, or YandexGPT. They take an informative stance, as an audience was mainly informed about an update, a change, or a new feature without taking a clear stance on the positive or negative implications for society or individuals. A small proportion of texts, about 4 per cent in each segment, covered related to AI industry promotional events, start-ups, investments, and conferences. However, they are mentioned twice as much in the business-oriented segment compared to the popular one; the latter raises the employment topic nearly twice as often.
Unique categories for each of the news outlets are 'Legal regulation' and 'Impending Danger'. The category 'Impending danger', which is characteristic of the popular press, includes articles that report potential dangers and disruptions that may be caused by AI technologies. These texts tend to emphasize the potential societal damage or severe consequences that may arise due to the unforeseen impacts of AI technologies. We see that dangers and threats arising from the AI implementation are among less important issues:
(18) Искусственный интеллект обвинили в усугублении климатического кризиса.
By contrast, 'Legal regulation' is in the focus of business oriented news outlets that report on legal initiatives, indicating the level of readiness for AI embracement.
Discursive strategies in AI media representation
Discursive strategies employed to construe AI in the public conscience and shape its perception by lay audiences include the following: an explanatory strategy that is used to build awareness of AI and help to experiment with it; a prognostic strategy that is used to construe future scenarios of AI implementation and the strategy of warning about negative consequences of AI development.
The texts employing the explanatory strategy use definitions of AI (19) or they instruct the reader how to use automation program, describe its capacity and warn about some limitations (20):
(19) Искусственный интеллект - компьютерная программа, которая принимает и анализирует данные, а затем делает выводы на их основе (kp.ru);
(20) Как пользоваться YandexGPT в России
Нужно запустить Алису и ввести запрос
«Алиса, давай придумаем...». Это работает как в текстовом чате, так и при голосовом взаимодействии. После этого помощник предложит перейти в соответствующий режим.
На мобильных устройствах в него можно также попасть, нажав на кнопку «Давай придумаем» (rbc.ru).
The explanatory strategy draws on the antropomorphic metaphor when explaining the nature of AI and describing the principles of machine learning by comparing it to the acquisition of knowledge by young children:
(21) Ребенок в раннем детстве сначала обучается не конкретным задачам, а общим понятиям и представлениям об окружающей действительности, об ее устройстве. <...> При обучении нейронных сетей специалисты сталкиваются с похожими ситуациями. Если перед решением конкретных задач нейронные сети аналогично обучить на очень большом объеме данных восприятию основных, необходимых для осознания действительности изображений с целью научиться классифицировать представленные на них различные сцены - темные, светлые, внутри помещения, вне и т. п. (kp.ru).
The development of AI as a potentially revolutionary technology is not the first time in history that due to technology society is facing structural social changes. Whereas some texts
depict present AI applications or abilities, some texts tend to depict future AI abilities and applications by employing a prognostic strategy. As the previous section show, the texts often condition the future by using Future forms of the verbs.
As Table 5 shows, half of the texts contain references to future AI abilities or applications:
(22) Это концепция искусственного разума [(AGI - artificial general intelligence)], который будет способен решать любые задачи и соперничать с гениями (vedomosti.ru);
(23) Искусственный интеллект будет находить нарушения на предприятиях по обращению с ТКО (vedomosti.ru).
The prognostic strategy is used to shape alternative futures as consideration of probable or desirable outcomes. This strategy is maintained by the topoi of inevitability, necessity and competition, the three cornerstones of the media discourse, where texts employ voices of government officials and business representatives. The stance taken in AI news coverage implies the necessity of its development that is closely linked to the concept of security, competition and leadership:
(24) Пропасть в технологиях становится все больше. Почему развитие ИИ дает России шанс выиграть в мировой конкуренции? (lenta.ru).
The topos of necessity implies that embracing AI requires the formation of the environment that will facilitate the process and helps to avoid or reduce negative consequences of AI implementation. For instance, the quotation of the President of the Russian Federation emphasizes the necessity of AI development and implementation:
(25) на сегодняшний день развитие искусственного интеллекта - это вопрос безопасности и выживания государства (lenta.ru).
In the quotation the issue of AI development equals the issue of state existence, the resulting imagery of which is that of the savior. The topos of necessity is maintained by the use of words with the modal meaning of necessity (нужно, необходимо, необходимость, e.g., необходимость формирования новой веб-экономики; необходимо усилить внедрение; необходимость обеспечения технологической независимости, etc.).
The topos of inevitability are represented in the following examples:
(26) Герман Греф: искусственный интеллект будет развиваться вместе с человеком (kp.ru);
(27) Путин: искусственный интеллект откроет новую главу истории человечества. Глава госуцар-ства подчеркнул, что предотвратить прогресс в этой области невозможно, поэтому следует обеспечить лидерство (kp.ru);
(28) За информационными технологиями будущее всего населения планеты. Хотим мы того или нет, но в течение всего нескольких ближайших лет искусственный интеллект существенно изменит и облегчит нашу жизнь (kp.ru).
The idea of inevitability is supported by phrases with the semantics of beginning, large scale and omnipresent implementation of AI technology, mentions of official documents and state programs for AI development, such as начало массовой цифровизации, повсеместное внедрение «умных» технологий, программа «Цифровая экономика РФ» etc. As we see, a highly interpretative flexible technology cluster like AI is transformed into a seemingly
Verb AF/NF Range Range, %
будет 265 (2 197,64) 121/243 49,79
будут 137 (1 136,14) 83/243 34,16
позволит 62 (514,16) 49/243 20,16
смогут 49 (406,36) 37/243 15,23
сможет 37 (306,84) 28/243 11,52
поможет 29 (240,50) 24/243 9,88
помогут 13 (107,81) 11/243 4,53
принести 5 (41,46) 5/243 2,06
научится 3 (24,88) 3/243 1,23
покажет 2 (16,59) 2/243 0,82
принесут 1 (8,29) 1/243 0,41
Table 5. The frequency and range of verbs in the future tense in the corpus
inevitable and desirable technological pathway through the use of media narratives.
The corpus under study comprises the discursive strategy of warning about AI negative consequences that vary from frauds to existential threats:
(29) И пока достижения GPT берут на вооружение киберпреступники, специалисты предупреждают: искусственный интеллект может создать куда более серьезные проблемы, угрожающие жизни на Земле как таковой (lenta.ru).
However, the proportion of the texts containing a negative stance is relatively small and it is outnumbered by those that accolade the technology.
Conclusion
To sum up, the results of the study offer an initial picture on how recent technological advances in the fields of AI are communicated to the public in the Russian-language media narratives that has been constructed by intercepting quantitative characteristics of the texts, construing AI imagery in the Russian news media discourse, topic modelling dispersed across the media segments in the AI corpus, and discursive strategies in AI media representation.
The comparison of the quantitative characteristics of the business-oriented and popular media texts in AICorpus revealed close similarity on the length of the texts and their motivation. The major bulk ofthe texts is around 99-300 words (53.4%); their function is to inform readers about advancements in the field and prospects of AI use. Long texts (above 1301-2000 words, 6.2%) lean to an explanatory strategy, they are aimed at building awareness of the topic but not stimulating public discussions on AI social values.
The strategies employed to portray AI concept were discovered in the topical focus of the texts. The ten themes identified across the AICorpus provide a broad perspective on societal attitudes towards AI and its generative technologies, though seem not to drastically vary with the category of the newspaper outlet. Both types of media showed the preference for the themes of 'Positive Capabilities of AI' and 'AI Development'. Nevertheless, the discursive representations of AI tend to be more negative in the popular outlets as the articles employ the
theme of 'Impending Danger' focusing on the negative consequences of AI implementation, while business-oriented press shows more preference for legal control, thus, implicitly recognizing fears projected on AI but depicting the fact that this risk is manageable. The analysis of the texts showed that they all establish AI as a given and massively favorable technical development that will change society and economy fundamentally. They stress the idea of equality and coexistence of AI and humans excluding the competition between them.
The analysis of discursive strategies in AI concept representations revealed that they establish AI as an inevitable and highly welcomed technological development by depicting AI as a competent solution to a range of societal problems. From our results, we can identify that there is a need for readers to seek greater explanation on what AI, ChatGPT, and LLMs are, given a small number of articles that employ the explanatory strategy. When visions around AI are announced in Russian news narratives, they are often embedded in a rhetoric of prospective potentials and emphasize that innovation involves inevitability, necessity and competition. Although AI seems to penetrate all spheres of life, Russian media outlets seem reluctant to articulate potentials, risks, and ethical challenges that go along with current AI developments. A small proportion of texts articulate that the AI implementation bears risks for human society; they raise the issue of security by drawing attention to the possibility of its use by fraudsters. It should be noted that, while the loss of jobs is mentioned in both types of media outlets, the articles emphasize the need for employees with competences in AI development. Thus, the Russia-language media texts embody the celebration of technological progress and conceal its problems and contradictions. These findings underscore the pivotal role of media discourse in shaping public perceptions of AI.
The study prompts reflections on news media practices in the Russian Federation and encourages future monitoring of the influence of social, cultural, and political contexts on AI concept representation during a period of technological change. This research provides relevant insights for policymakers, AI developers, and educators to support balanced public engagement with AI technologies.
NOTE
1 The study is supported by Russian Science Foundation, Linguocultural characteristics of socially relevant phenomena in media discourse: corpus-based approach № 23-28-00509, https://rscf.ru/ project/23-28-00509/
Исследование выполнено за счет гранта Российского научного фонда № 23-28-00509, https:// rscf.ru/project/23-28-00509
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Information About the Authors
Elena Yu. Ilyinova, Doctor of Sciences (Philology), Professor, Department of Translation Studies and Linguistics, Volgograd State University, Prosp. Universitetsky, 100, 400062 Volgograd, Russia, [email protected], [email protected], https://orcid.org/0000-0002-3310-4020
Larisa A. Kochetova, Doctor of Sciences (Philology), Professor, Department of Translation Studies and Linguistics, Volgograd State University, Prosp. Universitetsky, 100, 400062 Volgograd, Russia, [email protected], [email protected], https://orcid.org/0000-0002-5278-7373
Информация об авторах
Елена Юрьевна Ильинова, доктор филологических наук, профессор кафедры теории и практики перевода и лингвистики, Волгоградский государственный университет, просп. Университетский, 100, 400062 г. Волгоград, Россия, [email protected], [email protected], https://orcid.org/0000-0002-3310-4020
Лариса Анатольевна Кочетова, доктор филологических наук, профессор кафедры теории и практики перевода и лингвистики, Волгоградский государственный университет, просп. Университетский, 100, 400062 г. Волгоград, Россия, [email protected], [email protected], https://orcid.org/0000-0002-5278-7373
