Научная статья на тему 'Socio-political content and regional discourse of modern Russia: the issues discussed by citizens in the online space and the solutions offered by the candidates for governor in their election manifestos (intersection points and fault lines)'

Socio-political content and regional discourse of modern Russia: the issues discussed by citizens in the online space and the solutions offered by the candidates for governor in their election manifestos (intersection points and fault lines) Текст научной статьи по специальности «СМИ (медиа) и массовые коммуникации»

CC BY
161
49
i Надоели баннеры? Вы всегда можете отключить рекламу.
Ключевые слова
ONLINE SPACE / SOCIO-POLITICAL CONTENT / REGIONAL DISCOURSE / MODERN RUSSIA / ISSUES DISCUSSED BY CITIZENS / SOLUTIONS OFFERED BY THE CANDIDATES FOR GOVERNOR / ELECTIONS MANIFESTOS / PARSING OF NETWORK DATA / MATHEMATICAL ANALYSIS OF SOCIAL NETWORKS / LINGUO-DISCURSIVE ANALYSIS / METHODS OF RELATIONAL SOCIOLOGY / ОНЛАЙН-ПРОСТРАНСТВО / СОЦИАЛЬНО-ПОЛИТИЧЕСКИЙ КОНТЕНТ / РЕГИОНАЛЬНЫЙ ДИСКУРС / СОВРЕМЕННАЯ РОССИЯ / ОБСУЖДАЕМЫЕ ГРАЖДАНАМИ ПРОБЛЕМЫ / ПРЕДЛАГАЕМЫЕ КАНДИДАТАМИ В ГУБЕРНАТОРЫ РЕШЕНИЯ / ИЗБИРАТЕЛЬНЫЕ МАНИФЕСТЫ / ПАРСИНГ СЕТЕВЫХ ДАННЫХ / МАТЕМАТИЧЕСКИЙ АНАЛИЗ СОЦИАЛЬНЫХ СЕТЕЙ / ЛИНГВОДИСКУРСИВНЫЙ АНАЛИЗ / МЕТОДЫ РЕЛЯЦИОННОЙ СОЦИОЛОГИИ

Аннотация научной статьи по СМИ (медиа) и массовым коммуникациям, автор научной работы — Ryabchenko N.A., Gnedash A.A., Malysheva O.P., Shestakova A.A., Nikolaeva M.V.

The article presents the findings of a major empirical study into socio-political user-generated content produced in the online space of the 22 constituent entities of the Russian Federation, as well as socio-political content generated by the candidates for governor of th same 22 constituent entities in 2018-2019. The methodological basis of the research into socio-political content and regional discourses of modern Russia is network analysis, whose theoretical basis is the methods of mathematical modelling of social networks and communities. At the second stage, network analysis was enhanced by cultural components of social action (local practices and contexts, discourses, repertoires and norms); therefore, the traditional methodology of mathematical analysis of social networks (i. e. structural network analysis) was supplemented with the methods of relational sociology and linguo-discursive analysis. Linguo-discursive analysis was used to interpret the processes of creation and development of meanings and contexts in political discourse generated both by social networks and communities, and candidates for governor likewise. Parsing of network data and linguo-discursive analysis was applied to research online political content generated in the online space of 22 constituent entities of the RF was carried out via operational hybrid toolset, a computer software program “Structural relational parsing of political content”. The empirical basis of the research comprises the content generated by the communities on Vkontakte social platform (online network communities “Tipichnyj” (“Typical”) representing 22 constituent entities of the RF and online network communities “Podslushano” (“Overheard”) representing 21 constituent entities of the RF), and the lectoral materials of the candidates for the governor in the 22 constituent entities of the RF. The analysis of research findings enabled us to group the regions according to the identified intersection points and fault lines in public discourse and the agenda shaped by the candidates for governor in their election programs. These are a “Zero tension” group, a “Prevention of Tension” group, a “Critical Tension”, and a “Prominent Tension” group. Such research into political content generated by people and governmental representatives in the regional public online space is highly warranted as it provides revolutionary new qualitative and quantitative insights into the socio-political situation of the RF regions; it can be applied to enhance effective communication between the authorities and civil society and allow to identify potential points of growth for protest sentiments capable of developing in online social networks and communities likewise.

i Надоели баннеры? Вы всегда можете отключить рекламу.
iНе можете найти то, что вам нужно? Попробуйте сервис подбора литературы.
i Надоели баннеры? Вы всегда можете отключить рекламу.

Социально-политический контент и региональный дискурс в современной России: что обсуждают граждане в online-пространстве, и что предлагают кандидаты на пост губернатора в предвыборных программах (точки пересечения и линии разлома)

В статье приводятся результаты масштабного эмпирического исследования социально-политического контента, продуцируемого гражданами в региональном онлайн-пространстве 22 субъектов РФ и социально-политического контента, созданного кандидатами на пост губернатора в тех же субъектах в период 2018-2019 гг. В качестве методологических оснований исследования социально-политического контента и регионального дискурса в современной России выступил сетевой анализ, базирующийся на методах математического моделирования социальных сетей и сообществ. На втором этапе исследования сетевой анализ был дополнен культурными компонентами социального действия (локальные практики и смыслы, дискурсы, репертуары и нормы); для этого классическая методика математического анализа социальных сетей, представляющая собой структурный сетевой анализ, была дополнена реляционной социологией и лингводискурсивным анализом. Лингводискурсивный анализ был необходим для изучения процессов интерпретации и создания значений и смыслов в политическом контенте, формируемом как социальными сетями и сообществами, так и кандидатами на пост губернатора. Парсинг сетевых данных и лингводискурсивный анализ осуществлялся при помощи разработанного гибридного операционального инструментария анализа политического контента в online-пространстве 22 субъектов РФ посредством программы для ЭВМ «Структурно-реляционный парсинг политического контента». Эмпирической базой исследования стали сообщества в социальной сети «ВКонтакте»: онлайн сетевые сообщества «Типичный» в 22 субъектах РФ; онлайн сетевые сообщества «Подслушано» в 21 субъекте РФ; материалы предвыборных кампании кандидатов на пост губернатора в 22 субъектах РФ. Проведенный анализ позволил разделить исследуемые регионы на группы в зависимости от выявленных точек пересечения и линий разломов в гражданском сетевом дискурсе и в предвыборных программах кандидатов на пост губернатора в online-пространстве 22 субъектов РФ: группа регионов «Нулевая напряженность»: группа регионов «Упреждение напряженности»; группа регионов «Критическая напряженность», группа регионов «Выраженная напряженность». Подобные исследования политического контента, формируемого гражданами и представителями власти в публичном региональном онлайн-пространстве, позволяют провести новый качественный и количественный анализ социально-политической ситуации в регионах РФ для эффективного выстраивания коммуникаций между властью и гражданским обществом, а также выявлять возможные точки роста протестных настроений в социальных сетях и сетевых сообществах в онлайн-пространстве.

Текст научной работы на тему «Socio-political content and regional discourse of modern Russia: the issues discussed by citizens in the online space and the solutions offered by the candidates for governor in their election manifestos (intersection points and fault lines)»

ЮЖНО-РОССИЙСКИЙ ЖУРНАЛ СОЦИАЛЬНЫХ НАУК. 2019. Т. 20. № 4. С. 27-48 i ПОЛИТИЧЕСКИЕ ИНСТИТУТЫ И ПРОЦЕССЫ

SOCIO-POLITICAL CONTENT AND REGIONAL DISCOURSE OF MODERN RUSSIA: THE ISSUES DISCUSSED BY CITIZENS IN THE ONLINE SPACE AND THE SOLUTIONS OFFERED BY THE CANDIDATES FOR GOVERNOR IN THEIR ELECTION MANIFESTOS (INTERSECTION POINTS AND FAULT LINES)

N. A. Ryabchenko, A. A. Gnedash, O. P. Malysheva, A. A. Shestakova, M. V. Nikolaeva

Natalia A. Ryabchenko. E-mail: rrrnatali@mail.ru. ORCID 0000-0001-6980-2894 Anna A. Gnedash. E-mail: anna_gnedash@inbox.ru. ORCID 0000-0002-3516-107X Olga P. Malysheva. E-mail: malisheva_83@mail.ru. ORCID 0000-0001-8285-0508 Anastasia A. Shestakova. E-mail: hahu1993@mail.ru. ORCID 0000-0003-3233-2029 Maria V. Nikolaeva. E-mail: masha_pershina93@mail.ru. ORCID 0000-0001-7002-1497 Kuban State University, Stavropolskaya St., 149, Krasnodar, 350040, Russia. Acknowledgements. The research was carried out through the financial support of the The Russian Foundation for Basic Research (Department of Humanitarian and Social Science), the research project no. 18-011-00910 entitled (The models and practices of political content management in modern states' online space in the "the Post-Truth" Era.) (2018-2020).

Abstract. The article presents the findings of a major empirical study into socio-political usergenerated content produced in the online space of the 22 constituent entities of the Russian Federation, as well as socio-political content generated by the candidates for governor of th same 22 constituent entities in 2018-2019. The methodological basis of the research into socio-political content and regional discourses of modern Russia is network analysis, whose theoretical basis is the methods of mathematical modelling of social networks and communities. At the second stage, network analysis was enhanced by cultural components of social action (local practices and contexts, discourses, repertoires and norms); therefore, the traditional methodology of mathematical analysis of social networks (i.e. structural network analysis) was supplemented with the methods of relational sociology and linguo-discursive analysis. Linguo-discursive analysis was used to interpret the processes of creation and development of meanings and contexts in political discourse generated both by social networks and communities, and candidates for governor likewise. Parsing of network data and linguo-discursive analysis was applied to research online political content generated in the online space of 22 constituent entities of the RF was carried out via operational hybrid toolset, a computer software program "Structural relational parsing of political content". The empirical basis of the research comprises the content generated by the communities on Vkontakte social platform (online network communities "Tipichnyj" ("Typical") representing 22 constituent entities of the RF and online network communities "Pod-slushano" ("Overheard") representing 21 constituent entities of the RF), and the lectoral materials of the candidates for the governor in the 22 constituent entities of the RF. The analysis of research findings enabled us to group the regions according to the identified intersection points and fault lines in public discourse and the agenda shaped by the candidates for governor in their election programs. These are a "Zero tension" group, a "Prevention of Tension" group, a "Critical Tension", and a "Prominent Tension" group. Such research into political content generated by people and governmental representatives in the regional public online space is highly warranted as it provides revolutionary new qualitative and quantitative insights into the socio-political situation of the RF regions; it can be applied to enhance effective communication between the authorities and civil society and allow to identify potential points of growth for protest sentiments capable of developing in online social networks and communities likewise. Keywords: online space, socio-political content, regional discourse, modern Russia, issues discussed by citizens, solutions offered by the candidates for governor, elections manifestos, parsing of network data, mathematical analysis of social networks, linguo-discursive analysis, methods of relational sociology

Introduction and research problem statement

The survey "Media consumption in Russia - 2019" (annually conducted since 2015) carried out by "Deloitte" Company proved that the online space is the most popular media channel as regards the increase in media engagement index (Tendencii monetizacii kontenta v Internete: Mediapotreblenie v Rossii, 2019). Notably, the results proved that the most active user category, concerning media consumption, is the youth between the ages of 16 and 19; the second most active user category is represented by senior citizens of over 65 and the middle-aged between the ages of 40 and 44. In 2019, the online space rated first with reference to the most popular source of news used by the RF citizens: 78% of respondents preferred the online space (news sites, analytical sites, official websites of various institutions); 58% - television; 37% - the online space (social networking sites and blogs). The highest level of credibility to online social networking sites is among the youth group between the ages of 16 and 29. In the group of senior citizens only 6% of respondents over 65 years of age show confidence in social networking sites. The most popular online networking platforms among citizens of the RF are YouTube (preferred by 86% of respondents), VKontakte (80% of respondents), Odnoklassniki (57% of respondents), Instagram (52% of respondents) and Facebook (44% of respondents). YouTube platform is predominantly used for consuming the content produced by media celebrities (37% of respondents) and ordinary people (32% of respondents). Vkontakte online social platform is predominantly used for consuming the content produced by friends (such content is preferred by 51% of respondents), and the content produced by social media communities (23% of respondents). Notably, the consumption rate of the content produced by non-famous ordinary users prevails over the consumption rate of the content produced by media sources and mass media sites, or media celebrities (preferred by 10%, 3% and 9% respectively). Facebook media platform is considerably different regarding the consumption rate of the content produced by media celebrities (such content is preferred by 16% of respondents) and unfamiliar ordinary users (17% of respondents). As for Instagram media platform, the content consumed by the users is predominantly produced by their friends (37% of respondents) and media celebrities (34% of respondents). The main sources of the most frequently consumed content are user's friends - 51%, media celebrities - 31%, unfamiliar users - 30%, social communities -19%, media companies and mass media - 15%.

The research findings describe and characterize the main trends of production and reproduction of information with regard to the features characteristic of the network society era:

- any person is capable of creating significant content;

- any content can transform into communication;

- the produced content is under the command of social media users rather than of its creator,

- such content includes a political content component which undergoes transformation from being the form of mass communication to becoming a form of personalized communication due to its target-oriented quality and the effect of "informational shift of balance".

The main research objective is finding answers to the following questions:

What are the intersection points of socio-political content generated by the citizens in the online space of the RF with the political content generated by the candidates for governor in their election programs, which were also posted on the Internet? What

discussion fields are generated by the citizens — users of online social communities? What problems do prospective heads of constituent entities of the RF identify in their regions? Are there intersection points in the governmental and public discourses, or are the authorities estranged from the people? Modern foreign studies identify a great number of micro targeting strategies used in election campaigns at different levels ranging from a local deputy to the President (New Directions, 2018; Strach, 2015; Ridout, 2015; Ridout, 2015; Smith, 2018). Thus, a question arises: do Russian politicians at a regional level adjust their election messages (the content they generate) to communicate with certain audiences within a constituent entity of the RF? To what extent is regional socio-political agenda manifested in the content of candidates and how does this agenda relate to the latent problems of the region? Can we regard fault lines in the discourse generated by the governors and online public discourse as potential points of political tension and the basis for the development of protest sentiment in the region?

The theory of production, creation and consumption of socio-political content in the online space

The primary conceptual basis of the research of socio-political content produced by citizens/ users, network communities and political constituents involves scientific concepts premised on the fact that the online space and the content generated in the given space against the background of developing network society is the main method and the primary platform of exerting influence on the citizens (Dowling, 2015; Soroka, 2015). Likewise, it is the main platform of consolidation and articulation of interests both for citizens and political constituent entities (Batorski, 2017; Pearson, 2017; Shomova, 2019; Martynov, 2012).

In post-informational society, communication in the online space causes certain action and practices in the offline space; interestingly, this action and practices are not always loyal towards the authorities of a particular city, region or a country (Stevens, 2012; Valentino, 2013; Theocharis, 2015). The events in St. Petersburg, London, Brussels, Washington and Tokyo revealed the methods which were used in the major cities — the cities with their own problems in the housing sphere, a huge number of citizens and different channels of communication in their structure — to organize their citizens during a terrorist act within a relatively short period of time (ranging from one to five hours). The Internet and social networks are able to organize people at any spot on the planet without the involvement of local and regional authorities within virtually an hour. This means that the emerging innovative types and channels of political networking mobilization can transform modern democratic managerial structures in complicated situations or emergencies and become the key mechanism of restructuring the interaction between the authorities and the civil society when solving global and local problems (González-Bailón, 2016; Jost, 2018). Besides, mobilizing signals in the form of various online content are not generally amplified and pass through conventional hubs of network communication (popular media, top bloggers, opinion leaders, media celebrities). It is ordinary users and network communities of local level that become the main information promotion tool. Such new types and channels of political mobilization can have both constructive and destructive implications for the development of socio-political systems on global and local levels (Theocharis, 2015; Wallis, 2016).

The theoretical framework of the presented study of socio-political content produced by the citizens / users, network communities or political constituent entities is an

explanatory model of political content management, i.e. of the process of political content generation, consumption and the influence it has on social reality both in the online and in the offline space (Ryabchenko, 2018).

The model is premised on the fact that political content produces continuous environment - an asynchronous multimodal discursive field comprising discrete messages which are used by institutionalized and non-institutionalized socio-political actors of public space for interaction. An asynchronous multimodal discursive field comprises asynchronous multimodal discourses which determine the vectors of development for the entire field and shape the possibility for the development of the potential to trigger social action. A distinctive feature of asynchronous multimodal online discourses is that verbal content is supported by other forms of content, such as emoji or video, which enhances its efficiency and focuses the attention of online audience on particular aspects, thus increasing the probability of accumulating the potential for social action.

Political content and asynchronous multimodal discourses which are produced by this content accumulate certain potential for social action. The realization of the potential for social action can result in various events, both in the online and in the offline space. The model of political content management, from the perspective of system analysis and network approach, is a system with a "feedback" mechanism, which is fulfilled through a "content - event - content" algorithm, and is capable of transforming socio-political systems. Thus, the analysis of political content and asynchronous multimodal discourses is highly warranted, during electoral cycles in particular.

The empirical research methodology and the stages of analysis of political content

The online space, due to the diversity of information and communication technologies it provides, enables the actors of the modern public space to be involved in continuous and diverse interactions, and the product of such interaction is continuously generated and reproduced content. Unlike offline political content, the content produced in the online space is being continuously transformed by the users of different social networking sites and communities, as it is the basis of their interaction (Jones, 2015). Concurrently, the political content transformations result in transformations of the social networking platforms and communities themselves, thus determining social and political action in the offline space.

A complex research into political content is based on network approach which is premised on the belief that socio-political processes and their participants are global social graphs. The notion of a social graph is based on the conception that the world is comprised of networks rather than groups; thus, the nature of the participants of network interaction is determined by the intersection of these networks (Naybet, 2009; Wong, 2011).

The methodological basis of the research into socio-political content and regional discourses of modern Russia is network analysis, whose theoretical basis is the methods of mathematical modelling of social networks and communities which enable the description and the featuring of the ongoing processes as transactions of scale-free networks. A network described through the theory of scale-free networks is visualized as a graph whose number of edges (links) adjacent to a vertex (a user) follows a power-law distribution. It has become common to call such networks complex networks, as only scale-free networks are capable of describing a manifestation of socio-political processes in the online space

(Smith, 1999; Thai 2011). The first stage of the research presupposes the use of network analysis to obtain a selection of free access data generated by 22 regional online social communities based on VKontakte social media platform, which are the main producers and consumers of political content. Each of these online social communities became a staging area for the people of the region and a platform for discussion. Being free from strict moderation, a social media community enables active discussion and high participation, as a user can bring a whatsoever post topic into discussion.

At the second stage, network analysis is enhanced by cultural components of social action (local practices and contexts, discourses, repertoires and norms); therefore, the traditional methodology of mathematical analysis of social networks (i. e. structural network analysis) is supplemented with the methods of relational sociology and linguo-discursive analysis. The combination of the methods of relational sociology is premised on the idea that socio-political reality should be regarded as dynamic and unfolding interactions or transactions, which are dynamic continuous processes inseparable from socio-political contexts. A transaction regarded as a dynamic process, rather than the constituent elements, becomes a primary unit of relational sociological analysis. Such approach enables a research of not only structural, but also cultural components (regarded as local practices and contexts, discourses, repertoires and norms), which are treated integrally. Network analysis enhanced by methods of relational sociology allows analyzing social networks and communities, which produce political content, as dynamic actors performing managerial operations through political content, rather than inert entities among static nodes (Maltseva, 2014; Mische, 2011; Wellman, 2008; Ryabchenko, 2019).

Linguo-discursive analysis is used to interpret the processes of creation and development of meanings and contexts in political discourse generated by social networks and communities. Linguo-discursive analysis comprises content analysis, discourse analysis, and semantic analysis of tags and hashtags (the folksonomy analysis).

Quantitative analysis of the content or content analysis is a specific research tool applying various formal procedures to identify the main patterns and regularities in arranging the information flow, to estimate the intentions of the communicator reproducing this flow, and predict probable reactions of the audience to this information. Such approach enables us to regard network data (including the media text) as an objective manifestation of interests and needs of the parties involved in communication. The specific feature of content analysis applied to network data is the procedures which Big Data involves, yet traditional methods of content analysis are irrelevant. Content analysis is inherently the calculation of frequency and the amount of occurrences of some units in a particular text. The received quantitative characteristics of network data enable conclusions to be drawn about the qualitative characteristics of the analyzed online content including implied meanings. Content analysis to research political content is justified, since online content, which is a first-order reality for content analysis, is always a product of human activity. Thus, it is possible to identify various implications or traces of social and psychological factors when analyzing political content. Therefore, by identifying, marking and registering the indicators or referents of these factors, we can identify and measure the factors themselves (Robinson, 2008; Kognitivnaja lingvistika, 2011; Katermina, 2018). On the whole, the aim of content analysis is to understand deeper intertextual reality; in other words, relying on such analysis we can objectively estimate a particular political event or a phenomenon and draw conclusions on its prospective potential. Thus, we can predict the trend in their development, as well as prospective public reactions.

Discourse analysis is a specific scientific method of research which has developed on the basis of many interdisciplinary theories: ethnography, social and cultural anthropology, linguistics, etc. Community is a key notion in most theoretic approaches to discourse analysis, as it determines the way social actors speak and think and, consequently, affects their participation in implementation of social practices. Discourse analysis theoretical constructs are inherently less speculative, and extend our overview of the nature, structural components and functions of the analyzed phenomena. Discourse analysis enables a particular text or an act of communication to be researched within the framework of extensive socio-political structures such as text corpuses, discursive patterns and historical context (Alefirenko, 2014; Crystal, 2003; Goroshko, 2013). One more distinctive feature of discourse analysis is that it regards socio-political reality as a continuously developing construct, rather than a static entity. Therefore, the emphasis is placed on the ways political content is generated, reproduced and consumed. Thereby, discourse analysis allows to study not only the ways a language creates, reflects and presents political content; rather, it reveals another side of this complex interaction — the processes of shaping and configuring the essence of communicative processes in the online and offline space.

Semantic analysis of tags and hashtags (folksonomy analysis) is a method used to analyze collective categorization of content (for example, texts, media texts, webpages, links, posts in blogs, images, audio recordings, photos, video clips, etc.) produced, reproduced and consumed in the online space through arbitrarily chosen tags, called tags and hashtags (Kahn, 2017; Ryabchenko, 2019). Folksonomy analysis in our research allowed to identify structural and qualitative characteristics of political content produced in the online space of 22 regions of the RF. Interpretation of the obtained results enabled us to construct a matrix of discourses and concepts specific for the ecosystem of the online space of 22 constituent entities of the RF.

We applied the methods of linguo-discursive analysis to identify the existing practices of political content creation and management, made a typology and analyzed regional online discourses which developed in the online space during the gubernatorial election cycle of 2018-2019.

Parsing of network data and linguo-discursive analysis, applied to research the political content generated in the online space of 22 constituent entities of the RF, was carried out via operational hybrid toolset (mathematical analysis of social networks and communities, linguo-discursive analysis, and methods of relational sociology) — a computer software program "Strukturno-relyacionnyj parsing politicheskogo kontenta" ("Structural relational parsing of political content") created and registered by N. A. Ryabchenko.

The results of the empirical research: intersection points and fault lines in public network discourse and election manifestos of the candidates for governor in the online space of 22 constituent entities of the Russian Federation

One of the most prominent events of 2018 in socio-political sphere was gubernatorial elections in 22 constituent entities of the RF, which is the main criterion of including the regions into the selection of the research. We developed an operational hybrid toolset (which includes mathematical analysis of networks and communities, linguo-discursive analysis and methods of relational sociology), which permits to analyze political content in the online space.

Figurel. Dataset "Typical" and "Overheard"

The empirical basis of the research is represented by the communities on Vkontakte social platform, viz. online network communities "Tipichnyj" ("Typical") representing 22 constituent entities of the RF, and online network communities "Podslushano" ("Overheard") representing 21 constituent entities of the RF (the online network community "Podslushano" is not present in the online space of Anadyr, the capital of Chukotka Autonomous Area); and the electoral materials of the candidates for the governor in the 22 constituent entities of the RF.

The data on the 22 constituent entities of the RF is divided into three datasets. "Typical" is the first dataset which comprises the posts added by the members of the online network community "Tipichnyj" ("Typical") on Vkontakte social platform. The period of the selection is the 1st of January 2018 to the 1st of June 2019, with the overall number of post accounting for approximately 160 000 units. The content of the selection is audio, video and textual information. The largest subset in the given dataset is "Tipichnyj Omsk" ("Typical Omsk"), while the smallest one is "Tipichnyj Pskov" ("Typical Pskov") (see fig 1). "Overheard" is the second dataset which comprises the posts added by the members of the online network community "Podslushano" ("Overheard") on Vkontakte social platform. The period of the selection is the 1st of January 2018 to the 1st of June 2019, with the overall number of messages accounting for approximately 190 000 units. The content of the selection is audio, video and textual information. The largest subset in the given dataset is "Podslushano Samara" ("Overheard Samara"), while the smallest one is "Podslushano Pskov" ("Overheard Pskov") (see fig. 1).

The third dataset comprises the electoral materials used by 105 candidates for governor during their election campaigns in constituent entities of the RF.

Frequency analysis allowed testing "Typical" and "Overheard" datasets to identify markers of asynchronous multimodal discourses which form the asynchronous multimodal discursive field of the constituent entities of the RF and, thus, determine the vectors of the development of the potential for social action. To research the identified asynchronous multimodal discourses, we applied linguo-discursive analysis.

The analysis showed that discursive fields "Typical" and "Overheard" are different as regards their stylistic features.

On average, the posts from a dataset "Typical" are impersonal and instructive (informing about the oncoming events in the city, the action taken, both in the city and the region; the action taken by the Government of the RF and local authorities); they are the posts by regional media sites; the posts include hyperlinks to other sites and resources (on average 50% of the posts); the posts include cliched phrases (such as Good morning, Ivanovo / [or the name of the city in question]! Have a nice day! — Доброе утро, Иваново! Отличного дня!); the posts are advertisements and rarely personal messages or surveys. The asynchronous multimodal discourses related to the "Typical" dataset are characterized by neutral and official style or informal style deprived of any sort of vulgarisms. We hereby conclude that the official discourse produced by traditional media and regional authorities exceeds the boundaries of media and governmental websites. This process is random, as it is not handled or controlled. For instance, in "Typical Samara" dataset 34% of the posts are news posted by 63.ru, kp.ru, RIA News. samara.kp.ru media sites.

The "Overheard" discursive field, unlike the "Typical" discursive field is characterized by a distinct time reference. To report past events, in personal posts users choose Present Tense, while media agencies opt for Past Tenses.

In some regions, such as Magadan, Nizhny Novgorod, Samara, Orel, Khabarovsk. Yakutsk, the posts from the "Overheard" dataset coincide in style with the "Typical" dataset. Also, messages in some groups — especially in such regions as Abakan, Vladivostok, Voronezh, Orel, Khabarovsk, Yakutsk — are characterized by emotive language or low-flown vocabulary, including colloquialisms and vulgarisms (for example, "starpery", "muzhiki","svini","koshmar","ubogie","bukhaet", " osel","idiot","vypendrezhnik", " pokhren", "postebat'sya", " dolbanutaia", "besit", "baba","babenka", "babentsiia", "babishcha", "babki", "pofig", "shlat'sia", "merkantil'noi, shliukhoi","bytovukha","nishchebrodiki", "nishchebrodka"). Asynchronous multimodal discourses related to the "Overheard" dataset seem to be face-to-face conversations, as regards graphical stylistic means used in posts: abundant ellipses, indicating wistfulness or a dramatic pause; multiple exclamations or question marks and emoticons, such as a smiley face, to indicate force of emotion, indignation or resentment, approval or exclamation. Supplemented with an occasional use of rhetorical questions for emphasis, these techniques create the feeling of a face-to-face conversation.

The discourses are characterized by multiple spelling and punctuation mistakes; blending of styles in the posts written by ordinary users indicates unofficial and non-regulated character of communication with boundless freedom when it comes to text formatting, yet putting some restrictions regarding the choice of the topic (the posts are usually subject to particular moderation rules and if users want to maintain confidentiality, they ask to post the message anonymously: "Uvazhaemyj moderator", "pozhaluista, anonimno" ("Dear moderator; please, [post the message] anonymously"). On the one hand, a discursive field "Overheard" functions just like an ancient Greek agora, a central public space where anybody irrespective of their social status could express their own opinion. On the other hand, moderation of "Overheard" network communities allows identifying the vector of the development of discourse, and, consequently, of a discursive field which is able to either initiate socio-political action in the offline space or weaken its capacity. The potential to trigger socio-political action, which shapes in discursive fields, is predominantly determined by the subject matter of a discursive field and its socio-political agenda, which, in its turn, is determined by the users themselves.

Socio-political issues of both datasets were articulated through the following asynchronous multimodal discourses:

- "Refuse reform" and its main markers: "no improvements", "increase in fees", "litter and wastes in cities", "unauthorized landfills", "discharge of toxic wastes. This discourse is the most relevant in the discursive fields of Tyumen, Omsk, Voronezh, Krasnogorsk;

- "Urban Public Amenities" and its main markers: "lack of parking lots", "unauthorized construction", "malpractice in construction", "construction of cathedrals instead of parks and squares and public resentment", "deterioration of heat distribution and water service networks", "stray animals", "heat distribution network". This discourse is a part of the discursive field of all the regions, presented in the selection of the research;

- "Quality of Roads" and its main markers: "poor quality of asphalt covering", "poor quality of mended roads". This discourse is a notable feature of the discursive field of Yakutsk — "absence of roads and transport links with the rest of Russia"; 'air and sledge dog transport", "out-of-repair state of public transport";

- "Environment" and its main markers: "water pollution", "air pollution". The citizens of Omsk region highlight the fact that the region is at the "bottom of the ecological rating". Overall, the asynchronous multimodal fields across all the regions are characterized by the discussion of the indices of various ratings, especially those issued by the Institute for Complex Analysis of Regional Problems. This discourse is a notable feature of the discursive field of Omsk, Barnaul, Orel;

- "Healthcare" and its main markers: "poor quality of medical services", "underequipped healthcare facilities", "mites", "contaminated food supplies", "respiratory and virus diseases", "rudeness of personnel". This discourse is a notable feature of the discursive field of Barnaul;

- "Criminogenic Situation" and its main markers: "crime", "drugs", "fraud", "terrorism", "missing people", "aggressive behavior of teenagers", "teenage drinking practices". This discourse is a notable feature of the discursive field of Yakutsk;

- "Culture" and its main markers: "shutdown of cultural centers", "shutdown of museums". This discourse is a notable feature of the discursive field of Vladimir;

- "Civic Participation" and its main markers: "resentment, skeptical or sarcastic attitude to reforms and price rise". This discourse is a notable feature of the discursive field of Moscow;

- "Pension Reform" and its main markers: "protests", "calls to protest", "protests against the retirement-age increase". This discourse is a notable feature of the discursive field of all the regions from the selection;

- "Criticism of the Authorities" and its main markers: "criticism for inaction", "criticism for the inaction of authorities", "criticism of the authorities for denouncing the resentful citizens who criticize authorities and accuse them of laziness". This discourse is a notable feature of the discursive field of Ivanovo - "protests against the administration of Ivanovo Region", and the discursive field of Krasnogorsk — public outcry caused by the appointment of Elmira Khaimurzina to replace the Governor of the region Radii Khabirov;

- "Economy of Regions" and its main markers: "debts of regions", "increase of debts or their reduction". This discourse is a notable feature of the discursive field of Orel (a debt of the region accounts for 18 bln. rubles), Yakutia and Khakassia;

- "Presidential Approval Rating" and its main markers: "V. Putin's presidential approval rating", "increase and decrease in approval". This discourse is a notable feature of the discursive field of Omsk, Yakutsk, Blagoveshchensk, Magadan;

- "Elections" and its main markers: "resentment about the agitation practices and pressure", "authorities secure attendance of the voters by arranging another significant event concurrent with the elections - voting on the major problems of the region - or by providing allowances" (for example, free transport and drawing game of iPhone in Omsk), "Sobchak", "Naval'nyj", "Grudinin", "anxiety about the Russian version of 'Maidan'". The discourse is a notable feature of the discursive field of Moscow;

- "Civil Servants and Corruption" and its main markers: "shocking behavior of civil servants, Duma members", "corruption and punishment", "civil servants' income" (for example, "Tyumen Mayor has substantially increased his income"), "bureaucracy" (for example, "minor republican civil servants restrain the development of Yakutia"). This discourse is a notable feature of the discursive field of Yakutsk, Orel, Tyumen;

- "Freedom of Speech" and its main markers: "abuse of an author law", "misuse of the Abuse of Authority Law". This discourse is a notable feature of the discursive field of all the regions from the selection;

- "Renovation Programme" is a characteristic feature of Moscow and Orel regions exclusively.

Linguo-discursive analysis applied to "Typical" and "Overheard" datasets allowed to identify the main thematic discourses which generate discursive fields. Also, we singled out the constituent entities with a focus on a particular theme or concept. The analysis showed that unique discourses characteristic of only one constituent entity are not common for the selection. All of the above described discourses are characteristic of all the regions from the selection, yet in each region there is a prevalent discourse which is characteristic of the discursive field of the corresponding constituent entity of the RF and which could trigger socio-political tension. For instance, the discourses characteristic of "Moscow" and "Moscow Region" discursive fields show that corruption and unfair attitude towards the citizens triggered socio-political action in the offline space which manifested itself through 2019 protests.

We compared and contrasted the received results with the findings obtained through the analysis of the third dataset - "Electoral content of the candidates for governor", which enabled the description of a common socio-political discursive field of the constituent entities of the RF that developed and was functioning during the gubernatorial election cycle. Besides, it allows identifying the intersection points and the fault lines between public interests and needs and electoral manifestos of the candidates for governor.

The "Electoral content of the candidates for governor" dataset comprises electoral material of 105 candidates for governor, among which 33 candidates positioned themselves by presenting an election program, 31 candidates positioned themselves through media coverage, and 12 candidates through interviews which they gave to regional media agencies. It is remarkable that 29 candidates did not present any material about their election campaign in the online space. Three out of these 29 candidates won the elections (Chukotka Autonomous Region, Kemerovo Region and Ivanovo Region); these 3 candidates are the representative entities of the regional nominating entity "Edinaya Rossia" ("The United Russia"), a Russian national political party, and had served as the Interim Governor of the respective region just before the elections).

We subdivided the "Electoral content of the candidates for governor" dataset according to the candidate positioning strategy: 1) "Direct Candidate Positioning", "Combined Candidate Positioning", and "Mediated Candidate Positioning".

The "Direct Candidate Positioning" dataset was found in 33 election programs of the candidates for governor of 16 constituent entities of the RF (Altai Territory - 1 out of

4 candidates, Amur Region - 2 out of 4, Vladimir Region - 1 out of 4, Voronezh Region -3 out of 6, Ivanovo Region - 1 out of 5, Magadan Region - 2 out of 4, Moscow Region -

2 out of 6, Moscow - 3 out of 5, Novosibirsk Region - 1 out of 4, Orel Region - 3 out of 5, Primorye Territory - 3 out of 9, Pskov Region - 1 out of 5, Samara Region - 1 out of 6, Republic of Sakha (Yakutia) - 2 out of 4, Khabarovsk Territory - 5 out of 5, Republic of Khakassia - 2 out of 4). The dataset includes electoral content, comprising the materials of the election program previously posted online. The analysis also showed that Khabarovsk Region is the only constituent entity which provided open access to the election programs of all the candidates.

5 out of 33 candidates from the above mentioned dataset won the elections (Altai Territory, Voronezh Region, Moscow, Republic of Sakha (Yakutia), Khabarovsk Territory).

3 candidates (Altai Territory, Voronezh Region, Republic of Sakha (Yakutia)) had served as Interim Governor of the region and are representative entities of the regional nominating entity "Edinaya Rossia" ("The United Russia") - a Russian national political party; a former Mayor of Moscow was a self-nominated candidate; a candidate from Khabarovsk Territory is a representative entity of the regional nominating entity "Liberal'no-Demokraticheskaya Partiya Rossii" ("The Liberal Democratic Party of Russia").

The analysis of the political content produced by the candidates who won the elections showed the following:

Altai Territory - Viktor Tomenko. The focus of the election program of the candidate is on the economic development of the region - "Altajstkij kraj. Energiya razvitiya" ("Altai Territory. The Energy of Development") - and on the wellbeing of the citizens. Instead of agitation techniques, the campaign office of the candidate encourages and collects suggestions and claims from the citizens in order to shape an innovative development agenda - to make the authorities open and accessible; to create open municipal forum platforms devoted to the discussion of the program and the priorities in the development of the region.

Voronezh Region. Alexandr Gusev, the Interim Governor, highlights some economic areas aimed at the development of manufacturing and agricultural sector of the region; a particular focus of the program is social security (modern housing), healthcare, education, road network improvement and culture.

Moscow. Sergey Sobianin presents a complex program of the development and provision with urban amenities for each district of the capital city called "Moj Rajon" ("My District").

Republic of Sakha (Yakutia). Aisen Nikolaev in the election program entitled "Yakutia Vpered" ("Yakutia Go Forward!") highlights the main vectors of the development of the region: employment as the foundations for wealthy life, well-being and self-respect; knowledgeability, intelligence and professional expertise are the foundations of leadership; high quality healthcare - healthy people; the economy of boundless opportunities; the construction of the Lensky Bridge; cities and villages of Yakutia — the beauty, comfort, safety; maintaining village lifestyle in Yakutia is a foundation of sustainable future for the indigenous peoples of the region.

Khabarovsk Territory. Sergey Furgal highlights the priorities in Governor's work: economy (privileges to local producers); healthcare; pension reform (against the increase of retirement age); transport network, social sector; industrial sector (timber, fish, mineral resources); housing services and utilities.

The "Mediated Candidate Positioning" dataset includes 31 candidates (Altai Territory — 3 out of 4 candidates, Amur Region - 1 out of 4, Vladimir Region - 1 out of 4, Voronezh

Region - 3 out of 6, Kemerovo Region - 1 out of 6, Krasnoyarsk Territory - 1 out of 3, Moscow Region - 4 out of 6, Moscow - 2 out of 5, Nizhny Novgorod Region - 1 out of 4, Novosibirsk Region - 2 out of 4, Omsk Region - 3 out of 4, Orel Region - 1 out of 5, Primorye Territory - 3 out of 9, Pskov Region - 2 out of 5, Tyumen Region - 1 out of 4, Samara Region - 1 out of 6, Republic of Khakassia - 2 out of 4). The candidates did not provide open access to their election programs. However, the issues and main directions of the development from the election programs of these candidates are presented as journalist interpretations and published as articles.

7 out of 31 candidates from the presented set won the elections (Krasnoyarsk Territory, Moscow Region, Nizhny Novgorod Region, Novosibirsk Region, Omsk Region, Orel Region, Republic of Khakassia). 6 out of 7 candidates had served as Governor of the Region before the elections (Krasnoyarsk Territory, Moscow Region, Nizhny Novgorod Region, Novosibirsk Region, Omsk Region, Orel Region). 4 out of 7 candidates (Krasnoyarsk Territory, Moscow Region, Nizhny Novgorod Region, Novosibirsk Region) are representative entities of the regional nominating entity "Edinaya Rossia" ("The United Russia"), a Russian national political party; 2 candidates (Orel Region, Republic of Khakassia) are representative entities of the regional nominating entity "Kommunisticheskaya partiya Rossijskoj Federacii" ("The Communist Party of the Russian Federation"); a candidate from Omsk Region is a self-nominated candidate.

Krasnoyarsk Territory. Aleksandr Uss makes investment prospects, environmental issues and maintaining social capital a priority of his election program.

Moscow Region. Andrei Vorobi'ev highlights environmental, healthcare and economic issues of the region as the main directions of his electoral campaign. He also emphasizes the necessity of stable healthcare construction and restoration of healthcare facilities, of attracting best healthcare specialists to the region and developing the potential capacity of the region; pure water supply for urban population; planting and keeping forests and water bodies of Podmoskovye (Moscow Region areas) clean, and creating an innovative waste industry of international standard.

Nizhny Novgorod Region. Gleb Nikitin advocates the development of a new system of interaction of civil society and authorities which presupposes massive discussion of programs and cooperation with civil councils and experts. The focus of his campaign is maintaining industrial and economic capacity, an increase in the amount of investment and creation of comfortable conditions for investors; improvement in the standard of living for the people, accessibility of healthcare and education and other social services.

Novosibirsk Region. Andrey Travnikov makes socio-economic agenda a priority of his election program: robust regional economy, development of social potential and generation of comfortable social environment, industrial development and support to small business, development of healthcare sector, construction of sports facilities, and development of education system.

Omsk Region. Aleksandr Burkov highlights the following objectives of his election program: an increase in population growth rate and birth rate; an increase in life expectancy to 80; maintenance of sustainable income growth; double decrease in poverty rates (improvement in housing conditions); acceleration in technological development; acceleration in introducing digital technologies in economics; development of more rapid rates of economic growth than world standards; solution to transport problem; environment; modernization of housing services and utilities; support to agricultural sector.

Orel Region. Andrey Klychkov highlights improvement of housing services and utilities, road network development, attraction of investment to the region, improvement of public amenities and public areas. A particular focus is put on eradication of corruption in public procurement.

Republic of Khakassia. Valentin Konovalov considers that the following steps should be taken: to impose higher taxes on the top business tycoons and increase the level of social responsibility for major corporations; to develop the industrial sector and create new workplaces; to support and enhance revival of the countryside; to reduce costs; to terminate cost reduction program in social sector.

"The Combined Positioning of the Candidate for Governor" - 12 candidates (Amur Region -1 out of 4 candidates, Vladimir Region - 1 out of 4, Ivanovo Region - 1 out of 5, Magadan Region - 1 out of 4, Primorye Territory - 2 out of 9, Pskov Region - 1 out of 5, Samara Region - 2 out of 6, Tyumen Region - 3 out of 4) did not provide open access to their election programs. However, they gave interviews and presented the main directions of the development of their election programs. 7 out 12 candidates who gave interviews about their election programs won the elections (Amur Region, Vladimir Region, Magadan Region, Primorye Territory, Pskov Region, Samara Region, Tyumen Region). 4 out of these 7 candidates (Amur Region, Magadan Region, Pskov Region, Samara Region) had served as Interim Governor of the region before the elections. 5 out of the 7 candidates are representative entities of the regional nominating entity "Edinaya Rossia" ("The United Russia"), a Russian national political party (Amur Region, Magadan Region, Pskov Region, Samara Region, Tyumen Region); 1 candidate is a representative entity of the regional nominating entity "Liberal'no-Demokraticheskaya Partiya Rossii" ("The Liberal Democratic Party of Russia") (Vladimir Region); a representative of Primorye Territory is a self-nominated candidate.

Amur Region. In the interview, Vasilii Orlov highlights the following directions of his program: reduction in migration processes to restrain the outflow of the young from the region; personnel replacements; reorganization of local government and government department systems; creation of a favorable image of the region for the western world through the development of some areas, such as tourism; with regard to economic sector — to develop the main strategies of the economic development of the region (agricultural sector), mining metallurgical industry, energy and tourism, space and development of Tsiolkovsky, and oil and gas chemical industry; the development of transport and energy infrastructure, road network system — (the Amur Bridge), the cable road "Zolotaya Milya" ("Golden Mile"), which is one of the main components of tourism development. Notably, Orlov is one of the first civil servants who created a Facebook account and administers it himself in order to be closer to people.

Vladimir Region. In the interview, Vladimir Sipiagin declares that the Region needs "muzhskaia ruka" ("a man's hand", i.e. "a strong leader and rigorous management and measures"), that would put the matters right. He also claims that he is going to "kalenym zhelezom vyzhigat' korruptsiiu" ("extirpate corruption"). He announces that his priorities are the improvement in the standard of living and an increase in average pay, an indicator which makes Vladimir Region rate last in ranking of the Central Federal District. The candidate highlights the economic issues of the region and claims that economic development can increase the level of tax collection, and improve the socioeconomic situation.

Magadan Region. In the interview, Sergey Nosov declares that his main objective is to make the region a comfortable place for living. A particular focus is made on the

road network system, preparation for a heating season, support to the provision with pharmacy medication. Notably, once it had been announced that Sergey Nosov was going to leave the post of the Head of Nizhniy Tagil, there appeared #HocoBHeyxogu hashtag ("#nosovdonotleave") in local social media.

Primorye Territory. In the interview, Oleg Kozhemiako highlights the issues in healthcare - lack of medical specialists and equipment; he places the focus on the renovation of school buildings and equipping healthcare facilities. He also highlights the issues of defrauded homebuyers' and certain actions taken; brings up a question of social payments to the under-privileged citizens; underlines the issues of the road network system. Notably, Oleg Kozhemiako is the only candidate who has been the Governor of more than three different constituent entities of the RF. The whole interview was devoted to featuring the candidate to his advantage: the questions posed by the interviewer illuminated the personality of the candidate, his hobbies and his accomplishments as Governor of different regions.

Pskov Region. In the interview, Michail Vedernikov touches upon social and economic issues of the region, socio-political situation, problems and prospects of development. A particular mention is given to problems which should be solved: governmental debt, the level of healthcare service and healthcare supplies, subsidized pharmaceutical provision of the citizens. He highlights the issues of public road network system, housing services and utilities, agricultural sector, development of small and medium-sized businesses, and the issue of preserving the industries.

Samara Region. The interview with Dmitrii Azarov is actually a working meeting with the President of the RF. They touch upon the issues of economic indices, employment issues, industrial sector, road network system, and maintenance of historic heritage.

Tyumen Region. In his interview, Alexandr Moor underlines the following priorities of his program: industrial sector, digital economy, agricultural sector, and support to small and medium-sized business. He promises to find a solution to such issues as decrease in administrative and tax burden for business on the regional level, as well as to do his best to solve the problem of rehousing the citizens from dilapidated and substandard housing. One of the highlights of his program is the development of "Sotrudnichestvo" ("Cooperation") Project with a particular focus on sovereignty of autonomous districts and development of healthcare facilities.

We apply relational network analysis to the data contained in all the datasets, to construct asynchronous multimodal discursive fields of the studied regions and identify the intersection points and fault lines in public network discourse of the regions and help find solution to the relevant socio-political issues. As a correlation measure, we chose the involvement of the region and the winning candidates into a particular sociopolitical discourse, and obtained the asynchronous multimodal discursive field "Regional socio-political agenda: region - discourse", constructed on the basis of the identified regional discourses (Figure 2).

The blue color identifies the constituent entities of the RF, included in the research selection. The red color identifies the discursive fields relating to the socio-political agenda which is shaping in the given constituent entities of the RF. The linking lines as well as their thickness define which issue is current for the region. We can observe that Healthcare issue is relevant for all the 22 regions of the RF, yet it is the most prominent issue for the discursive field of Altai Territory. The discourse relating to Presidential Approval Rating is more prominent for the discursive field of Altai Territory, Amur Region,

Figure 2. Asynchronous multimodal discursive field "Regional socio-political agenda: region -

discourse"

iНе можете найти то, что вам нужно? Попробуйте сервис подбора литературы.

Magadan Region, Omsk Region, Republic of Sakha (Yakutia). Fugure 2 shows the vectors of the development of the discursive fields of the analyzed constituent entities of the RF.

Figure 3 features the findings of network relational analysis visualized as an asynchronous multimodal discursive field "Regional socio-political agenda: governor — discourse".

The red color identifies the discursive fields generated by regional discourses. The yellow (mediated positioning of the candidate), the green (combined positioning of the candidate) and the blue colors (direct positioning of the candidate) identify the candidates who won the elections of 2018 and became governors in 22 constituent entities of the RF.

We compare the correlation of the online discursive fields produced by the elected governors with the online discursive fields produced by the citizens of the analyzed regions. The links featured by Figure 3 determine the degree of involvement of a particular governor into a regional discourse, likewise, his involvement or non-involvement into the asynchronous multimodal discursive field of the region. By combining discursive fields "Regional socio-political agenda: region — discourse" and "Regional socio-political agenda: governor — discourse", we can identify the intersection points and fault lines in public online discourse and the objectives proposed by the elected governors in their election programs. Three of the elected governors did not generate any content about

Figure 3. Asynchronous multimodal discursive field "Regional socio-political agenda:

governor — discourse"

their election campaign in the online space, viz. representatives of Chukotka Autonomous Area, Kemerovo Region and Ivanovo Region. Therefore, for these Russian Federation regions, we can not identify fault lines and, as a result, can not identify potential triggers of protest sentiments among the region population.

The asynchronous multimodal discursive field "Regional socio-political agenda: intersection points and fault lines — Altai Territory". The intersection point is "Environment", the fault line is "Healthcare" (Figure 4.).

The asynchronous multimodal discursive field "Regional socio-political agenda: intersection points and fault lines" of all considered regions are presented in Table 1.

Discussion and conclusions

The conducted analysis makes it possible to sort the studied regions into certain groups according to the identified intersection points and fault lines in public network discourse and election manifestos of the candidates for governor in the online space of 22 constituent entities of the Russian Federation.

The first group — "Zero tension". The asynchronous multimodal discursive field is not characterized with spiking discursive phenomena on the identified socio-political issues. This group comprises: Samara Region, Pskov Region, Khabarovsk Territory, Nizhny

Figure 4. "Regional socio-political agenda: intersection points and fault lines — Altai Territory"

Novgorod Region, Novosibirsk Region and Chukotka Autonomous Area. The absence of spiking phenomena shows the absence of tension points which could trigger protest sentiments in the regions.

The second group — "Prevention of Tension". It includes the regions where fault lines are absent (or the intersection points surpass the number of fault lines by one), and there are intersection points in public discourse and the agenda shaped by regional authorities. This group comprises: Krasnoyarsk Territory, Republic of Khakassia, Orel Region and Moscow.

The third group — "Critical Tension". It embraces the regions where the number of fault lines is equal to the number of intersection points, or there are no intersection points at all. This group comprises: Primorye Territory, Tyumen Region, Altai Territory, Vladimir Region and Magadan Region. Any negative socio-political event can become a tension point and trigger protest sentiments, especially relating to the issues representing fault lines.

The fourth group — "Prominent Tension". Here belong the regions where the number of fault lines surpasses the number of intersection points by more than one. This group comprises: the Republic of Sakha, Voronezh Region and Omsk Region. These are the regions with high potential for protest sentiments; in there, a substantial fault line between the public discourse and the agenda shaped by regional authorities is observed.

Such research into political content generated by people and governmental representatives in the public regional online space enables a revolutionary new qualitative and quantitative analysis of socio-political situation of the RF regions, which could enhance effective communication between the authorities and civil society and allow to identify potential points of growth for protest sentiments developing in social networks and communities in the online space.

Table 1. The asynchronous multimodal discursive field "Regional socio-political agenda: intersection points and fault lines"

The intersection points The fault line

Altai Region "Environment" "Healthcare"

Amur Region Not identified "Presidential Approval Rating"

Vladimir Region Not identified "Culture"

Voronezh Region Not identified "Economy of Regions", "Culture", "Healthcare", "Quality of Roads", "Urban Public Amenities".

Krasnoyarsk Territory "Refuse Reform" not identified

Magadan Region not identified "Presidential Approval Rating"

Moscow "Civil Servants and Corruption", "Renovation Programme" "Elections"

Moscow Region "Refuse Reform" "Criticism of the Authorities"

Omsk Region "Environment" "Elections", "Presidential Approval Rating", "Refuse Reform"

Republic of Khakassia "Economy of Regions" not identified

Orel Region "Civil Servants and Corruption", "Economy of Regions" "Renovation Programme"

Primorye Territory not identified "Environment"

Republic of Sakha "Economy of Regions", "Quality of Roads" "Civil Servants and Corruption", "Presidential Approval Rating", "Criminogenic Situation"

Tyumen Region "Civil Servants and Corruption" "Refuse Reform"

Nizhny Novgorod Region, The discursive field is not characterized with spiking discursive Novosibirsk Region, phenomena on the identified socio-political issues

Pskov Region, Samara Region, Khabarovsk Territory

References

Ackland, R., Gibson, R. (2013). Hyperlinks and Networked communication: A comparative Study of Political Parties Online. International Journal of Social Research Methodology, 16(3), 231-244. DOI: http://dx.doi.org/10.1080/13645579.2013.774179; Alefirenko, N. F. (2014). Formation and Development of Discourse Linguistic Theory. XLinguae.

European Scientific Language Journal, 7(2), 32-45. Batorski, D., Grzywinska, I. (2017). Three Dimensions of the Public Sphere on Facebook. Information, Communication & Society, 1, 1-19. DOI: http://dx.doi.org/10.1080/1369118X.2017.1281329 Bennett, W.L., Segerberg, A. (2012). The Logic of Connective Action: Digital Media and the Personalization of Contentious Politics. Information, Communication & Society, 15(5), 739-768. DOI: http://dx.doi.org/10.1080/1369118x.2012.670661

Crystal, D. (2003). Language and the Internet. Cambridge University Press.

Dowling, C. M., Wichowsky, A. (2015). Attacks without Consequence? Candidates, Parties, Groups, and the Changing Face of Negative Advertising. American Journal of Political Science, 59(1), 19-36.

González-Bailón, S., Wang, N. (2016). Networked Discontent: The Anatomy of Protest Campaigns in Social Media. Social Networks, 44, 95-104. DOI: https://doi.org/10.1016Zj.socnet.2015.07.003

Goroshko, E. A. (2013). Lingvistika novyh medij kak odin iz vyzovov lingvisticheskoj tradicii proshlogo [Linguistics of New Media as One of the Challenges of the Linguistic Tradition of the Past]. Moscow.

Harlow, S. Harp, D. (2011). Collective Action on the Web: A Cross-cultural Study of Social Networking Sites and Online and Offline Activism in the United States and Latin America. Information, Communication & Society, 15(2), 196-216. DOI: http://dx.doi.org/10.1080/1369118x.2011.591411

Jones, R. H., Chik, A., Hafner, C.A. (2015). Discourse and Digital Practices: Doing Discourse Analysis in the Digital Era. N.Y.: Routledge.

Jost, J. T., Barbera, P., Bonneau, R., Langer, M. (2018). How Social Media Facilitates Political Protest: Social Media and Political Protest. Political Psychology, 39(11), 85-118. DOI: 10.1111/pops.12478

Kahn, E. V. (2017). Heshtegi kak novoe lingvisticheskoe javlenie [Hashtags as a new Linguistic Phenomenon]. Filologicheskij aspekt [Philological Aspect], 1(21), 91-98.

Katermina, V. V., Gnedash, A. A. (2018). Formirovanie politicheskogo kontenta v onlain-prostranstve: strukturno-setevoi i lingvodiskursivnyi analizy sovremennykh social'nykh dvizhenii (na primere "WOMEN'S MARCH") [Formation of Political Content in Online Space: a Structural-Network and Linguodiscursive Analyses of Modern Social Movements (as exemplified by "Women's March")]. Politicheskaja lingvistika [Political Linguistics], 4(70), 103-111.

Kognitivnaja lingvistika (2011). [Cognitive linguistics]. St. Petersburg: Filologicheskij fakul'tet SPbGU [Faculty of Philology, St. Petersburg State University].

Maitseva, D. V. (2014). Relyacionnaya sociologiya: novyj etap v razvitii analiza social'nyh setej ili samostojatel'noe napravlenie? [Relational Sociology: a New Stage in the Development of the Analysis of Social Networks or an Independent Direction?]. Monitoring obshhestvennogo mnenija: Ekonomicheskie i social'nyeperemeny [Monitoring Public Opinion: Economic and Social Changes], 4(122), 3-14.

Martynov, K. (2012). Ot slaktivizma k respublike: pochemu internet revolyucii stanovyatsya re-al'nost'yu [From Slaktivism to the Republic: Why Internet Revolution is Becoming a Reality]. Logos [The Logos], 86(2), 19-27.

Masías, V. H., Hecking, T., Hoppe, H. U. (2018). Exploring the Relationship Between Social Networking Site Usage and Participation in Protest Activities. Frontiers in Applied Mathematics and Statistics, 4 DOI: https://doi.org/10.3389/fams.2018.00056

Mische, A. (2011). Relational Sociology. Culture, and Agency. In J. G. Scott, P. J. Carrington (Eds.) The SAGE Handbook of Social Network Analysis (pp. 80-98). SAGE Publications.

Naybet, T., Roda, K. (2009). Virtual'nye social'nye prostranstva: podkhody, praktiki, perspektivy [Virtual Social Spaces: Approaches, Practices, Prospects]. Sociologicheski ezhegodnik: sb. nauch. tr. [Sociological Yearbook]. Moscow: RAN.

Ridout T. N. (ed.) New Directions in Media and Politics (2018). London: Routledge.

Pearson, M. (2017). Teaching Media Law in a Post-truth Context: Strategies for Enhancing Learning about the Legal Risks of Fake News and Alternative Facts. Asia Pacific Media Educator, 27(1), 17-26. DOI: https://doi.org/10.1177/1326365X17704289

Ridout, T. N., Fowler, E. F., Branstetter, J., Borah, P. (2015). Politics as usual? When and why traditional actors often dominate YouTube campaigning. Journal of Information Technology & Politics, 12(3), 237-251.

Ridout, T. N., Franz, M., Fowler, E. F. (2015). Sponsorship, disclosure, and donors: Limiting the impact of outside group ads. Political Research Quarterly, 68(1), 154-166.

Robinson, P., Nick, C. E. (2008). Handbook of Cognitive Linguistics and Second Language Acquisition. New York And London: Routledge.

Ryabchenko, N. A., Gnedash, A. A., Malysheva, O. P. (2019). Upravlenie politicheskim kontentom v social'nyh setyah v period predvybornoji kampanii v epohu postpravdy [Presidential Campaign in the Post Truth Era: Innovative Digital Technologies of Political Content Management in Social Networks]. Politicheskie issledovaniia [Polis. Political Studies], 2, 92-106. DOI: https://doi. org/10.17976/jpps/2019.02.07. Ryabchenko, N. A., Katermina, V. V., Gnedash, A. A., Malysheva, O. P. (2018). Politicheskj kontent social'nyh dvizhenij v onlajn-prostranstve sovremennyh gosudarstv: metodologiya analiza i issledovatel'skaya praktika [Political Content of Social Movements in the Online Space of Modern States: Methodology of the Analysis and Research Practices]. Yuzhno-rossijskij zhurnal social'nyh nauk [South-Russian Journal of Social Sciences], 3, 139-162. Ryabchenko, N. A., Katermina, V. V., Gnedash, A. A., Vulfovich, B. G. (2019). Regional'nyj politicheskij diskurs: teoreticheskaya model', metodologiya issledovaniya i praktiki upravleniya politicheskim kontentom v onlajn-prostranstve subektov RF [Regional Political Discourse: Theoretical Model, Methodology of Research and Practice of Managing Political Content in the Online Space of the Russian Federation Subjects]. Politicheskaja lingvistika [Political Linguistics], 5(77), 114-131. DOI 10.26170/pl19-05-12 Shomova, S. A. (2019). Vybory prezidenta RF — 2018 v zerkale memov: novye realii politicheskoj kommunikativistiki [Presidential Election in the Mirror of Memes: New Realities of Political Communication]. Politicheskie issledovaniia [Polis. Political Studies], 3, 157-173. DOI: https://doi. org/10.17976/jpps/2019.03.10 Smith D. N., Hanley E. (2018). The Anger Games: Who Voted for Donald Trump in the 2016 Election, and Why? Critical Sociology, 44(2), 195-212. DOI: https://doi.org/10.1177/0896920517740615 Smith, M., Kollack, P. (1999). Communities in Cyberspace. London: Routledge, Soroka, S., McAdams, S. (2015). News, Politics, and Negativity. Political Communication, 32(1), 1-22. Stevens, D. (2012). Tone Versus Information: Explaining the Impact of Negative Political Advertising. Journal of Political Marketing, 11(4), 322-352. Strach, P., Zuber, K., Fowler, E. F., Ridout, T. N., Searles, K. (2015). In a Different Voice? Explaining the Use of Men and Women as Voice-Over Announcers in Political Advertising. Political Communication, 32(2), 183-205. Tendencii monetizacii kontenta v Internete: Mediapotreblenie v Rossii (2019). [Internet Content Monetiza-tion Trends: Media Consumption in Russia — 2019]. Retrieved from https://www2.deloitte.com/ru/ ru/pages/technology-media-and-telecommunications/articles/media-consumption-in-russia.html Thai My, T., Pardalos Panos, M. (2011). Handbook of Optimization in Complex Networks: Communication and Social Networks. New York: Springer. The Age of Perplexity: Rethinking the World we Knew. (2018). New York: Penguin. Theocharis, Y. (2015). The Conceptualization of Digitally Networked Participation. Social Media +

Society, 1(2), 1-14. DOI: https://doi.org/10.1177/2056305115610140 Theocharis, Y., Lowe, W., Deth van J.W., García-Albacete, G. (2015). Using Twitter to Mobilize Protest Action: Online Mobilization Patterns and Action Repertoires in the Occupy Wall Street, Indignados, and Aganaktismenoi Movements. Information, Communication & Society, 18(2), 202-220. DOI: https://doi.org/10.1080/1369118X.2014.948035 Valentino, N. A., Nardis, Y. (2013). Political Communication: Form and Consequence of the Information Environment. In L. Huddy, D. O. Sears, & J. S. Levy (Eds.) The Oxford Handbook of Political Psychology (pp. 559-590). Oxford: Oxford University Press. Wallis, J., Given, L. M. (2016). #digitalactivism: New Media and Political Protest. First Monday,

21(2). DOI: http://dx.doi.org/10.5210/fm.v21i2.5879 Wellman, B. (2008). The Development of Social Network Analysis: a Study in the Sociology of

Science by Linton C. Freeman. Contemporary Sociology, 37(3), 221-222. Wong, S., Altman, E., Rojas-Mora, J. (2011). Internet Access: Where Law, Economy, Culture and Technology Meet. Computer Networks, 55(2), 470-479.

Received 15.10.2019 Accepted 14.11.2019

For citation: Ryabchenko N. A., Gnedash A. A., Malysheva O. P., Shestakova A. A., Nikolae-va M. V. Socio-Political Content and Regional Discourse of Modern Russia: The Issues Discussed by Citizens in The Online Space and The Solutions Offered by The Candidates for Governor in Their Election Manifestos (Intersection Points and Fault Lines). — South-Russian Journal of Social Sciences. 2019. Vol. 20. No. 4. Pp. 27-48.

социально-политический контент и региональный дискурс в современной россии: что обсуждают граждане в online-пространстве, и что предлагают кандидаты на пост губернатора в предвышорны1х программах (точки пересечения и линии разлома)

Н. А. Рябченко, А. А. Гнедаш, О. П. Малышева, А. А. Шестакова, М. В. Николаева

Рябченко Наталья Анатольевна.

Эл. почта: rrrnatali@mail.ru. ORCID 0000-0001-6980-2894 Гнедаш Анна Александровна.

Эл. почта: anna_gnedash@inbox.ru. ORCID 0000-0002-3516-107X Малышева Ольга Петровна.

Эл. почта: malisheva_83@mail.ru. ORCID 0000-0001-8285-0508 Шестакова Анастасия Андреевна. E-mail: hahu1993@mail.ru. ORCID 0000-0003-3233-2029 Николаева Мария Витальевна.

E-mail: masha_pershina93@mail.ru. ORCID 0000-0001-7002-1497

Кубанский государственный университет, Ставропольская ул., 149, Краснодар,350040, Россия.

Финансирование. Исследование проведено при финансовой поддержке Российского фонда фундаментальных исследований (отделение гуманитарных и социальных наук), проект № 18-011-00910 «Модели и практики управления политическим контентом в online-пространстве современных государств в эпоху постправды» (2018-2020 гг. под руководством Н.А. Рябченко).

Аннотация: В статье приводятся результаты масштабного эмпирического исследования социально-политического контента, продуцируемого гражданами в региональном онлайн-пространстве 22 субъектов РФ и социально-политического контента, созданного кандидатами на пост губернатора в тех же субъектах в период 2018-2019 гг. В качестве методологических оснований исследования социально-политического контента и регионального дискурса в современной России выступил сетевой анализ, базирующийся на методах математического моделирования социальных сетей и сообществ. На втором этапе исследования сетевой анализ был дополнен культурными компонентами социального действия (локальные практики и смыслы, дискурсы, репертуары и нормы); для этого классическая методика математического анализа социальных сетей, представляющая собой структурный сетевой анализ, была дополнена реляционной социологией и лингводискурсивным анализом. Лингводискурсивный анализ был необходим для изучения процессов интерпретации и создания значений и смыслов в политическом контенте, формируемом как социальными сетями и сообществами, так и кандидатами на пост губернатора. Парсинг сетевых данных и лингводискурсивный анализ осуществлялся при помощи разработанного гибридного операционального инструментария анализа политического контента в online-пространстве 22 субъектов РФ посредством программы для ЭВМ «Структурно-реляционный парсинг политического контента». Эмпирической базой исследования стали сообщества в социальной сети «ВКонтакте»: онлайн сетевые сообщества «Типичный» в 22 субъектах РФ; онлайн сетевые сообщества «Подслушано» в 21 субъекте РФ; материалы предвыборных кампании кандидатов на пост губернатора в 22 субъектах РФ. Проведенный анализ позволил разделить исследу-

емые регионы на группы в зависимости от выявленных точек пересечения и линий разломов в гражданском сетевом дискурсе и в предвыборных программах кандидатов на пост губернатора в online-пространстве 22 субъектов РФ: группа регионов «Нулевая напряженность»: группа регионов «Упреждение напряженности»; группа регионов «Критическая напряженность», группа регионов «Выраженная напряженность». Подобные исследования политического контента, формируемого гражданами и представителями власти в публичном региональном онлайн-пространстве, позволяют провести новый качественный и количественный анализ социально-политической ситуации в регионах РФ для эффективного выстраивания коммуникаций между властью и гражданским обществом, а также выявлять возможные точки роста протестных настроений в социальных сетях и сетевых сообществах в онлайн-пространстве.

Ключевые слова: онлайн-пространство, социально-политический контент, региональный дискурс, современная Россия, обсуждаемые гражданами проблемы, предлагаемые кандидатами в губернаторы решения, избирательные манифесты, парсинг сетевых данных, математический анализ социальных сетей, лингводискурсивный анализ, методы реляционной социологии

Статья поступила в редакцию 15.10.2019 Статья принята к публикации14.11.2019

Для цитирования: Рябченко Н. А., Гнедаш А. А., Малышева О. П., Шестакова А. А., Николаева М. В. Социально-политический контент и региональный дискурс в современной России: что обсуждают граждане в online-пространстве, и что предлагают кандидаты на пост губернатора в предвыборных программах (точки пересечения и линии разлома). — Южнороссийский журнал социальных наук. 2019. Т. 20. № 4. С. 27-48.

© 2019 by the author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

i Надоели баннеры? Вы всегда можете отключить рекламу.