Discursive Fields and Linguistic Patterns in the Online Space  

Комуникация и общество

Communication and Society

Natalia Ryabchenko

Kuban State University, Russia

E-mail: rrrnatali@mail.ru

Olga Malysheva

Kuban State University, Russia

E-mail: malysheva_op@mail.ru

Veronika Katermina

Kuban State University

E-mail: veronika.katermina@yandex.ru

Anna Gnedash

Kuban State University, Russia

E-mail: anna_gnedash@inbox.ru

 

Abstract: The digital context of the “post-truth” era conditioned by the networkization and digitalization of all spheres of life transforms network content which forms the basis of discursive fields in the online space. Applied research of D. Trump’s election campaign in March–June 2020 on Twitter to analyze discursive fields and linguistic patterns allowed the authors to find answers to the following questions: How do discursive fields arise in the online space? How are discursive fields and linguistic patterns formed in the online space during the election campaigns? What methods can be used to study the development of discursive fields and predict their impact on offline practices of citizens and groups of citizens? How do discursive fields affect citizens’ electoral behavior? How are vectors of destructive influence formed in discursive fields in the online space? The investigated case of D. Trump’s election campaign visualizes how dis­cursive fields and the linguistic patterns they produce initiate socio-political actions / practices aimed at supporting or protesting the current president and candidate for the presidency of the United States in the context of the latent deployment of immigration issues and under pressure the spreading pandemic “COVID-19”. In conclusion, it is stated that the strategy of using recurrent messages to form discursive fields in the online space in the face of urgent internal state problems and a global external threat leads to the emergence of “gaps” at the border of the core of the discursive field.

Keywords: online space, election campaign, Donald Trump, network vi­sual analysis, linguistic discursive analysis, recurrent messages.

DOI: 10.55206/IXSR6007

Introduction and Statement of Research Questions

The socio-political online space being a global discursive space in terms of the features of the formation of social action both online and offline is formed by a set of discursive fields, the interaction of which provides a connection between socio-political subjects and forms the sphere of their functioning. Under the discursive field in the online space we will understand the multidimensional (relative to network data) continuous social interaction regarding the definition of reality and its individual aspects. In his work “Consumption as a discourse” V.I. Ilyin says that any sociocultural field has a discursive character [1]. The discursive field can in some cases be a by-product of the interaction of individuals (a discursive field that arises during a period of crisis or emergency situations, for example, “coronavirus”), and in others, interaction is built inside the discursive field (for example, in influencer accounts). Accordingly, we can say that discursive fields in the online space can be formed spontaneously and constructed intentionally.

Discursive fields, both online and offline, have and are separated by boundaries. Within the boundaries of the demarcation line of the discursive field, the interaction may weaken or stop altogether, however, within the boundaries of the discursive fields in the online space, a significant increase in interaction is possible compared to the center (core) of the discursive field. This can mean either the absorption of one field by another, or their merging, which becomes possible due to one of the characteristics of the online space – hypertecturability. In addition, unlike offline discursive fields, online discursive fields do not have geographical barriers; thanks to the presence of hashtags, language boundaries are erased, and cultural practices are transformed. In fact, discursive fields in the online space exist within the framework of one global communication system with local features. This explains the processes of replication of certain types of social practices from one discursive field to another. We call social practices the results of the transformation of verbal and non-verbal (emoji) interaction within the discursive field.

The colossal array of network data1 (“BigData” type) generated by discursive fields in the online space does not allow for instantaneous or timely tracking of destructive changes in the socio-political sphere. The result is the registration of both constructive and destructive practices of the influence of discursive fields on the development of socio-political systems after the fact. During the election campaign of 2016-2017 in France, the mayor of Bordeaux and the candidate for the primaries from the right-wing Republican party Alain Juppe refused to run for president in view of the discursive field formed in the online space “Ali Juppe is an accomplice of Islamic extremists”; in 2016, during the election campaign for the presidency of the United States, the online discursive field “Pizzagate” was formed (the linguistic pattern “Don’t vote for Clinton, vote for Trump, because Clinton supporters are engaged in defamatory activities” and a side pattern “Save the children from pedophiles”), which led to a drop in H. Clinton’s rating and a shooting at a Comet Ping Pong pizzeria; tragic events in India in 2018, provoked by the widespread fake discursive field “Pakistani immigrants in India kidnap children” (linguistic pattern “Pakistani migrants/strangers on the streets of your cities are a threat”) [2], [3], [4], [5].

Modern information and communication technologies transform social reality and pose new research questions for science related to how exactly discursive fields and the content contained in them, and the linguistic patterns produced by them, initiate social action that affects the development and transformation of socio-political systems of modern states. How do discursive fields arise in the online space? How are discursive fields and linguistic patterns formed in the online space during election campaigns? What methods can be used to study the development of discursive fields and predict their impact on the offline practices of citizens and groups of citizens? How do discursive fields influence the electoral behavior of citizens? How are vectors of destructive impact formed in discursive fields in the online space? The solution to these issues can be the application and development of the methods of network linguistics and relational sociology as well as the development of methods for analyzing discursive fields as part of predictive analytics.

Theory and methodology for the study of discursive fields and linguistic patterns

The theoretical foundations for the study of discursive fields are based on the works of M. Foucault [6], [7] as well as the works of foreign [8], [9], [10], [11] and domestic researchers [12], [13], [14], [15] representing the inter­pre­tation, understanding and development of the ideas proposed by the philosopher. M. Foucault was engaged in understanding the idea of how some discourses formed and created systems of meanings that acquired the status of “truth” and dominate the ways of self-determination of people and the organization of the social space around us while others marginalized, being “enslaved”, although they had the potential to challenge, refute, or resist “suppressive” discursive practices. Thus, he developed the idea of a discursive field as a sphere of interaction between language, social institutions, subjectivity and power [16], [17]. Discursive fields, such as law or family, contain competing and contra­dictory discourses that have different “power” to fill with meanings and organize social institutions and processes. [18]

Until recently, Russian researchers spoke about discourses and discursive practices rather than about discursive fields implying deeper sociolinguistic constructs and not sharing these concepts. [19] With the release of V.I. Ilyin’s “Consumption as a discourse” [20] an approach has been outlined in Russian science according to which discursive fields as a set of discourses are perceived from the point of view of “power” and the potential to create a social context, a concept sphere, a framework for a certain social interaction, for example, the discursive field of Christian culture, the discursive field of press releases of local governments, the discursive field of international relations of various countries.

The study of communication and discourses in the online space is a hot topic. Researchers study a wide range of issues: technology-mediated commu­nication in political discourse [21], [22], [23]; the role of microblogging and social platforms in the online space [24], the prospects for the development of “electronic democracy” (e-democracy), its forms and practices of its use [25]; rhetorical features of political communication and tactics of targeted impact in political campaigns and social initiatives [26]; prospects and features of the development of democracy, promotion of politics, assertion of values and ideology in the online space and through it [27]; ways to promote political participation in and through the online space [28]; methods and forms of political management in the online space [29]; the level of political freedom and the degree of government representation in the online space [30]; patterns of representation of discourses in the online space. [31]

The study of data arrays generated in the online space is impossible without methods of automated data selection and processing. In this connection, the approach that consists of the use of text corpora in discursive studies (Corpus-Assisted Discourse Studies (CADS)) becomes relevant [32], [33]. The use of the complex CADS method in the study of discourses and discursive fields makes it possible to accumulate sociocultural knowledge about a certain corpus of texts and their sets (for example, analysis of the structuring of the image of refugees in British newspapers [34]). Thus, combining the methods of discursive analysis and the methods of corpus linguistics makes it possible to develop certain algorithms and create tools for sampling and analyzing Big Data

The structure-forming component of the discursive field in the online space is the network discourse that arises when a certain phenomenon of reality begins to be discussed, cited, replicated, and transformed, saturated with personal assessments and opinions (comments) of users of the online space. [35], [36] The discursive field in the online space is formed as a reaction of a set of social actors to reality when the number of participants in discursive interaction reaches the “critical number” of users. [37] In this case, a “force field” of the network discourse is formed which involves influencing the social and cognitive attitudes of the participants in discursive interaction – their reactions, opinions and, in the end, decision-making.

Discursive fields in the online space have a certain structure and charac­teristics/properties. Since the discursive field exists in the online space, it has a network architecture and constantly produces network data the analysis of which allows us to identify and explore the structural levels of the discursive field: “the level of participants/users”, “the semiotic level”.

The user level includes both real people and bots (a specialized program that performs automatically and/or according to a given schedule any actions through interfaces intended for people [38]). It is necessary to analyze both human users and bots within the discursive fields, and to identify their relationship within one or more related discursive fields since bots are one of the main ways to manipulate the discursive field.

The semiotic level is formed by linguistic means of the lexical sublevel, paralinguistic means of the graphic and phonation-graphic sublevels. Linguistic means of the lexical level is a set of the most frequent lexical units used in the discursive field. Paralinguistic means of the graphic and phonation-graphic levels include two types of hashtags. The first type of hashtags contains a lexical unit graphically represented by lowercase alphanumeric characters and belongs to the graphic sublevel. The second type contains units represented by uppercase alphanumeric characters. Since digital etiquette which reflects the practice of using capital letters as a marker of expressiveness provides for the use of capital letters as an indicator of the loudness of the speaker’s voice, units containing uppercase alphanumeric characters constitute the phonation-graphic level.

The level of users and the semiotic level produce each other and conti­nuously interact forming a multi-layered network.

The structure of the discursive field in the online space is determined by the core, core center, periphery, and boundaries. The application of the methods of structural and relational analysis, as well as graph theory, makes it possible to represent the discursive field in the online space, as well as its components, in the form of scale-free networks (global social graphs), and thus assess the current socio-political state of the online space and conduct predictive analytics of the possibility of transformations and vectors of development of socio-political systems, especially during the period of electoral cycles. [39]

The core of the discursive field, both at the level of users and at the semiotic level, can be identified and analyzed by constructing and visualizing a social graph where nodes are users of the online space or elements of the semiotic level (hashtags, the most frequent lexical units), and edges are connections between users, elements of the semiotic level, including connections between levels. The nodes that have the most connections (edges) in the visualization have a larger diameter than others and are called hubs (in terms of network structure), or opinion leaders, “influencers” (in terms of influencing socio-political content).

The center of the discursive core at the semiotic level is determined by the main theme or set of topics that prevail in each discursive field and are de­termined by the most frequent keywords (or key hashtags), as well as combi­nations of keywords (or hashtags) isolated from user messages. At the user level, the center of the discursive core is determined by users who have the maximum number of connections and are (from the point of view of the network structure) hubs.

The periphery of the discursive field is an area of the discursive field characterized by a weakening of interest and activity of users in the discussion of the central topic (topics).

The boundaries of discursive fields in the online space, as well as offline, are characterized by a change in the categorical apparatus that determines the correct interpretation of meanings. However, unlike offline discursive fields, online discursive fields have the resource to accumulate the potential to form an alternative core. This leads to blurring of boundaries, integration, and absorption of one discursive field by an adjacent one, as noted above. At the boundaries of the discursive field, there is a change in key categories or, at least, in the meaning invested in them. This leads to the fact that the field boundary is a zone of limited understanding or complete misunderstanding. Users functioning within the center of the discursive field have sufficient sociopragmatic competence [40]; they are fully capable of understanding and analyzing the totality of meanings and contexts that saturate a certain discursive field and can successfully interact within this field. The likelihood that the conclusions they draw in the process of absorbing certain content is rational is very high. However, users involved in the discursive field and interacting with it on its periphery have insufficient sociopragmatic competence due to the fragmentation of knowledge about the content of the situation and the intentions of the speakers. In view of this, the occasional introduction of outsiders into the discursive field (users who do not fully possess socio-pragmatic competence within a certain discursive field) may cause an incorrect interpretation of the topics under discussion and the con­clusions being made. This is due to their “discursive blindness” [41], discursive ignorance and imaginary contiguity of the discussed topics, which can sub­sequently cause destructive practices in offline space. [42], [43]

The boundaries of discursive fields in the online space, unlike discursive fields in offline, are permeable despite the difference in categorical apparatus. As a result, discursive fields can enter various kinds of interactions: intersect or integrate (enrich, strengthen each other and expand) with other discursive fields, enter into opposition with each other; in the future – to be absorbed, replaced or blurred / dispersed by other discursive fields.

Discursive fields in the online space tend to institutionalize, form online communities, which, if supported by socio-political factors offline, acquire sufficient potential to cause social action offline, contribute to the development of socio-political practices (constructive or destructive potential) and cause transformation of socio-political systems. The level of institutionalization is determined, among other things, by the fact that the discursive field in the online space has a “power character”, that is, it has the property of pragmatic saturation, in terms of influencing the cognitive attitudes of participants in the discursive field and social discursive practices in online and offline spaces. The pragmatic richness or “power character” of the discursive field is ensured by the presence of “filter bubbles” inside the discursive field that generate a high density of the same type of online content information context. This is because the technical features of building an online space (the tendency to send users individualized requests based on the analysis of user preferences) and the socio-cognitive characteristics of users (determination of reality due to an individual picture of the world and the circulation of certain information/thoughts within an isolated community) allow discursive fields to produce linguistic patterns.

An important distinguishing feature of the discursive space in the online space is the high potential for initiating the transformation of socio-political systems through the production of complex, stable constructs – linguistic patterns. Linguistic patterns that have a repetitive emotional component and have an effective impact on the recipient’s figurative thinking, their cognitive attitudes including decision-making determine the high pragmatic potential and stable tone of the discursive field that generates this pattern. The linguistic pattern is defined by us as a hybrid linguocognitive construct and a property of network discourse which manifests itself as pragmatic redundancy in the form of a socio-cognitive gestalt, which is formed due to the functioning of a continuous / stable discursive field and has a high potential to cause social action in online and offline spaces. We also highlight innovative communication patterns as a stable, repetitive functioning (interaction, intersection, integration and opposition) of discursive fields in the context of the activities of social communities and social media in the post-truth era, both online and offline, which leads to social action (constructive or destructive potential). ) through the control/manipulation of public opinion, and which can be represented as scale-free networks and applied to their study of the methods of structural and relational analysis.

 

Empirical research: how discursive fields and linguistic patterns are formed in the online space

In the period from March 1, 2020 to June 30, 2020, our research group collected and formed for further analysis a dataset of network data “Trump” dedicated to the pre-election campaign of D. Trump in order to analyze the discursive fields and linguistic patterns that are produced during this campaign as well as studies of their influence on socio-political processes in the country and the world. The specified data collection period was chosen because the studied socio-political processes were transformed not only because of the election campaign, but also under the pressure of the spreading COVID-19 pandemic.

The dataset consists of network data obtained by the method of continuous sampling (uploading) of messages published by users of the social network Twitter and containing the keyword “trump” through the Twitter application programming interface (API Twitter). The network data included in the obtained empirical database contains the following data arrays: messages published by users; the dynamics of responses (retweets) to published messages; data about users who publish these messages with the fixation of interaction to analyze their activity as a social graph; hashtags used by users for marking and classifying information in social networks; frequently used words and phrases, with the fixation of interaction to analyze the activity of their use as a social graph. The data was collected on the social network Twitter which is the third most popular social platform in the United States (the first place – Facebook, the second place – Pinterest, the third place – Twitter). [44] Among other things the choice of this social network is since the number of subscribers to the official page of D. Trump (@realdonaldtrump) on Twitter is 85.8 million readers [45], and on the social network Facebook – almost 31 million subscribers. [46] The collected empirical data made it possible to describe the discursive fields and linguistic patterns formed during the election campaign of D. Trump in the online space in 2020, against the backdrop of a global cataclysm – the coronavirus pandemic.

Network data from the generated dataset was analyzed and visualized using the Gephi program (an open-source software for network analysis and visualization of network data). To visualize the network data, the stacking algo­rithm “ForceAtlas” was used. (Network data is data that is characterized by participation in a relationship and due to this forms some links with each other. Meaningful representation of network data occurs by constructing social graphs based on them. As a rule, the data collected from social networks and services is network-based. Laying is a way to assign coordinates to each vertex/node of the social graph. The stacking reflects the topology of the social graph. For each vertex, the forces acting on it from all other vertices are calculated. Close vertices form clusters.).

As a result of processing the block “frequently used words and phrases, with fixing the interaction to analyze the activity of their use as a social graph” of the resulting network dataset, we were able to build and visualize the discursive field “Trump” for its further analysis and interpretation; the discursive field we have received is visualized and presented at the semantic level.

In Figure 1, on the left side, we see a visualization of the discursive field “Trump” obtained from network data collected in March 2020; on the right side of the figure is a visualization of the discursive field “Trump” obtained from network data collected in June 2020.

Figure 1. Visualization of the discursive field “Trump”

The discursive field “Trump” in March 2020 included 22,395 keywords that users most often used in their messages dedicated to D. Trump. These words are the nodes of the social graph that forms the basis of the discursive field in March 2020, formed in the online space during the election campaign. These words interact with each other forming 563823 connections thus allowing to highlight the main themes that exist in the discursive field. In June 2020, the Trump discursive field contained 28,354 words and 587,980 links. In the resulting discursive fields, we identified the center of the core of the discursive field, the core itself and the border of the discursive field, as well as the emergence and degree of penetration of an alternative discursive field into the discursive field under study (Figure 1). The core and center of the core are tweets, retweets, discussions and comments of the D. Trump team and his supporters, as well as opponents (who are fewer compared to users who support the incumbent president and candidate D. Trump).

 

Figure 2. Visualization of the discursive field “Trump”: the distribution of languages

In Figure 2, we see the linguistic composition of the Trump discursive field in March and June 2020, respectively. Analyzing the discursive field in dy­namics, one can observe a decrease in the use of English by 18.29%, with an increase in the percentage of Spanish by 9.95%, Italian by 1.3%, French by 1.24%, Turkish by 1.03%, Portuguese – by 1%. Analysis of the linguistic composition of the discursive field allows us to identify the characteristics and features of this field as well as to explore the emergence and degree of pe­netration of an alternative discursive field.

A visual and linguo-discursive analysis of the Trump discursive field in March and June 2020 shows that the Trump discursive field is dynamic, i.e., it changes over time under the influence of the external environment, socio-political events, and other discursive fields. So we can note in March the blurring of the boundaries of the discursive field “Trump” (upper part of the left part of figure 1). The trend of blurring the edges increases over time (upper part of the right part of Figure 1). This means that in the discursive field “Trump” there is a high potential for the formation of an alternative core (because of the emergence and penetration of an alternative discourse) which can lead to a change in key categories and meanings that are different from the meanings and key categories that function in the core of the discursive field.

Under the influence of what is the blurring of the boundaries of the analyzed discursive field and the formation of an alternative discursive field, is there an active transformation of the existing discursive field? To answer this question, let us analyze additional properties of the discursive field, including analyzing keywords from the point of view of belonging to a particular language spoken and written by users from the analyzed dataset.

The detected transformation of the discursive field and the visible gap associated with the emergence, strengthening and penetration of an alternative discursive field may be the result of a conscious strategy of the US presidential candidate’s team aimed at targeting content according to the linguistic and ethnic composition of voters. However, out of the total number of tweets included in the core and center of the core of the discursive field and posted from the official accounts of D. Trump in the period from May 2009 to September 2020 (53136 units), the unit “immigration” and “migrants” are found in 465 tweets, which is 0.87% of the total messages. In total, 8966 messages were posted in 2020 and only 26 of them are related to US immigration policy. In posts and retweets on immigration, D. Trump uses the combinations “illegal immigration” and “illegal immigrants” in 44% of the total number of tweets. In describing his position and the attitude of his supporters, whom D. Trump supports (Diana Harshbarger, Jim Bognet, Randy Weber, Mark Amodei, Roger Williams, Thom Tillis, Jim Renacci, Matt Gaetz, Brian Kemp), he uses the units “tough” and “strong” in an approving context when it comes to intransigence against illegal immigration, protecting the borders, the American people, and the Second Amendment to the US Constitution, for example:

– «Jun 25, 2020 05:20:42 PMSenator Thom Tillis of North Carolina has really stepped up to the plate. Thom is tough on Crime, Strong on the Border and fights hard against Illegal Immigration. He loves our Military, our Vets and our great Second Amendment. I give Thom my Full and Total Endorsement!»;

– «Sep 23, 2020 03:48:01 PMDiana Harshbarger (@DHarshbargerTN1) will be a champion in Congress for the People of Tennessee! She is Strong on Immigration, Jobs, Life and the Second Amendment. Diana has my Complete and Total Endorsement! #TN01 https://t.co/1BPtlfr9DT [Twitter for iPhone]».

The lexemes “dangerous”, “extreme”, “insane”, “shameful” are typical in describing the essence and consequences of illegal immigration as well as in condemning the actions of opponents who consider a more loyal US migration policy to be the right course, for example:

– «May 16, 2020 05:21:32 AMRT @Jim_Jordan: House Democrats would rather give tax dollars to illegal immigrants than American steelworkers. Shameful. https://t.co/A3… [Twitter for iPhone] link»;

– «Mar 16, 2020 12:01:31 AMRT @TimMurtaugh: Joe Biden would set an extreme and dangerous course on illegal immigration. Wouldn’t deport anyone except convicted felons. Says local police should not cooperate with ICE. In Biden’s America, EVERY city is a sanctuary city»;

– «Mar 16, 2020 12:01:50 AMRT @parscale: Joe Biden is a train wreck on illegal immigration and would harm national security. He’d have ZERO deportations for the first 100 days INCLUDING CRIMINALS and after that deort only felons. Trashes the rule of law and makes us less safe. Insanity!»

At the same time, the expressions “illegal immigration” and “illegal immigrants” are used in combination with the unit “crime” in 19% of cases which indicates the politician’s conviction that illegal immigration is dangerous for people and the state as well as a direct connection with the level of crime. Based on this and the results of visual analysis and linguodiscursive analysis of the discursive field “Trump”, we can conclude that the revealed transformation of the field is not a consequence of the implementation of a conscious strategy of the US presidential candidate’s team aimed at targeting content according to the linguo-ethnic composition of voters. The transformation of the discursive field “Trump” is the result of the formation and functioning of an “aggressively tuned”, alternative discursive field in relation to the analyzed field (the discursive field formed by the team of D. Trump himself and his active users/supporters). This alternative discursive field arose since the main means of forming a purposeful controlled discursive field “Trump” are recurrent messages that serve as a means of reorienting public attention, distorting / falsifying the picture of public opinion and, thus, creating an information cascade consisting of false or fake messages. Recurrent messages are identical repetitive messages posted on behalf of different users in social networks in the form of comments on a specific post. The purpose of posting such messages is the creation or transformation of existing cognitive attitudes and the formation of new patterns of social action of users, which is achieved through sustainable repetitive exposure.

So, if we single out the hubs of the English part of the discursive field “Trump” (Figure 3) and subject the messages to linguodiscursive analysis that contain the linkages “keyword “Trump” + hub”, then we will see:

 – the ratio of recurrent messages in the sample containing a bunch of keywords “Trump-President” – 83% of the total;

– the ratio of recurrent messages in the sample containing a bunch of keywords “Trump-People” – 80.5% of the total number of messages;

– the ratio of recurrent messages in the sample containing a bunch of keywords “Trump-Coronavirus” – 81.3% of the total number of messages;

– the ratio of recurrent messages in the sample containing a bunch of keywords “Trump-Media” – 45% of the total number of messages;

– the ratio of recurrent messages in the sample containing a bunch of keywords “Trump-Donald” – 70% of the total number of messages

Figure 3. Hubs of the English part of the Trump discursive field

The percentage of repeated messages in the discursive field or in a certain part of it indicates that the discursive field is mainly formed through recurrent messages and is a component of the campaign of D. Trump, his supporters, and his opponents. This strategy is only winning if the elements of the campaign are controlled and serve the interests of the voters. But in the case of a latent flow of unresolved political and administrative issues (for example, the migration issue and the issue of the “non-white” population of the United States) and the emergence of a global cataclysm or pandemic, as in the case of the coronavirus, it is impossible to form a stable discursive field only through recurrent messages. This will lead to the disappearance of several problematic socio-political issues from the field of view of the political campaign, the solution of which will give rise to alternative discursive fields that have a high potential to initiate transformations of socio-political systems through the production of complex, stable constructs – linguistic patterns. And, as a result, it can lead to the emergence of various discursive fields and social movements of a protest orientation, for example, such as Black Lives Matter.

 

Discussion and conclusion

The strategy of using recurrent messages to form discursive fields in the online space is inefficient and leads to “breaks” at the boundary of the core of the discursive field. These gaps create conditions for the emergence of alternative discursive fields that have a high potential to initiate transformations of socio-political systems offline. Recurrent messages, in addition to the identified “breaks”, produce linguistic patterns that lead to the emergence of an information cascade consisting of false or fake messages.

Understanding the fundamental principles of the functioning of discursive fields as a sphere of functioning of network discourses allows us to identify triggers and determine the point of formation of a “critical number” of users, leading to the formation of a certain stable discursive field; to define and describe a complex set of linguistic means that ensures the purposeful formation of a certain linguistic pattern within the discursive field; to typologize discursive fields depending on their constructive and/or destructive potential to influence the development of socio-political systems; influence the constructive and destructive vectors of development of discursive fields in the online space

The development of a linguistic model of socio-political communication in the online space as a system of purposeful functioning of discursive fields and linguistic patterns will enable their typology in terms of constructive and de­structive potential of influence; will allow describing the structures and identi­fying the key components of the linguistic pattern, which determine its high pragmatic potential; identify linguistic patterns of destructive potential and neutralize their influence in the discursive online space of modern states; as well as to identify linguistic patterns of constructive potential and enhance their influence to strengthen the development of socio-political systems in the post-truth era.

 

Notes: The research is given a financial support by The Russian Foundation for Basic Research (Department of Humanitarian and Social Science), the research project № 20-012-00033 entitled “Linguistic models of sociopolitical communication in online space: discursive fields, patterns and hybrid methodo­logy of network data analysis” (2020-2022).

Acknowledgment

The research is given financial support by The Russian Foundation for Basic Research (Department of Humanitarian and Social Science), the research project № 20-012-00033 entitled “Linguistic models of sociopolitical communi­cation in online space: discursive fields, patterns and hybrid methodology of network data analysis” (2020-2022).

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  • Рябченко, Н. А., Малышева, О. П. (2019). Трансформация социально-полити­ческой коммуникации: структурно-реляционный анализ феномена Fake News. Материалы IX международной социологической Грушинской кон­ференции «Социальная инженерия: как социология меняет мир», 20—21 марта 2019 г. / отв. ред. А. В. Кулешова. Москва: АО «ВЦИОМ», 46– [Ryabchenko, N. A., Malysheva, O. P. (2019). Transformacija social’no-politicheskoj kommunikacii: strukturno-reljacionnyj analiz fenomena Fake News. Materialy IX mezhdunarodnoj sociologicheskoj Grushinskoj konferencii «Social’naja inzhenerija: kak sociologija menjaet mir», 20—21 marta 2019 g. / otv. red. A. V. Kuleshova. Moskva: AO «VCIOM», 46–52.]
  • Katermina V. & Gnedash A. (2020) Linguistic models of social and political communication in the online-space: cognitive and pragmatic aspect. SHS Web of Conferences. 88(2): 01003. DOI:10.1051/shsconf/20208801003.
  • Foucault, M. (1977). Discipline and Punish. New York: Pantheon Books, 318.
  • Foucault, M. (1981). The Order of Discourse. Untying the Text: A Post–structuralist Reader. Ed. Robert Young. Boston: Routledge & Kegan Paul, 48–
  • Weedon, C. (1987). Feminist Practice and Poststructuralist Theory. Oxford: Blackwell Publishing, 187.
  • Diamond, & Quinby, L. (1988). Feminism & Foucault: Reflections on Resistance. Boston: Northeastern University Press, 246.
  • Snow, D. (2013). Discursive Fields. The Wiley-Blackwell Encyclopedia of Social and Political Movements. Oxford: Blackwell Publishing Ltd. DOI: 10.1002/ wbespm072.
  • Maglione, G. (2014). Discursive fields and subject positions: becoming ‘victim’, ‘offender’ and ‘community’ in restorative justice. Restorative Justice. Vol. 2. Issue 3: 327-348. DOI: 10.5235/20504721.2.3.327.
  • Ревзина, О. Г. (2005). Дискурс и дискурсивные формации. Критика и се­миотика. Новосибирск, Вып. 8: 66–78. [Revzina, O. (2005). Diskurs i diskur­sivnye formacii. Kritika i semiotika. Novosibirsk. № 8: 66–78.]
  • Макарычева, М. Г. (2006). Дискурс как предмет изучения в сфере между­народной политики (пример американо-российских отношений). Вестник Нижегородского университета им. Н.И.Лобачевского. Серия: Социальные науки. 1: 567– [Makarycheva, M. G. (2006). Diskurs kak predmet izuchenija v sfere mezhdunarodnoj politiki (primer amerikano-rossijskih otnoshenij). Vestnik Nizhegorodskogo universiteta im. N.I.Lobachevskogo. Serija: Social’nye nauki. 1: 567–574.]
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  • Foucault, M. (1981). The Order of Discourse. Untying the Text: A Post–structuralist Reader. Ed. Robert Young. Boston: Routledge & Kegan Paul, 48–
  • Weedon, C. (1987). Feminist Practice and Poststructuralist Theory. Oxford: Blackwell Publishing, 187.
  • Карасик, В. И. (2004). Языковой круг: личность, концепты, дискурс. Москва: Гнозис, 390. [Karasik, V. (2004). Jazykovoj krug: lichnost’, koncepty, diskurs. Moskva: Gnozis, 390.]
  • Ильин, В. И. (2007). Потребление как дискурс. http://www.old.jourssa.ru/ 2007/CB5/Ilyin.pdf. дата обращения 20.03.2022. [Il’in, V. I. (2007). Potreblenie kak diskurs. http://www.old.jourssa.ru/2007/CB5/Ilyin.pdf. data obrashhenija 20.03.2022.]
  • Brundidge, J. (2010). Encountering «Difference» in the Contemporary Public Sphere: The Contribution of the Internet to the Heterogeneity of Political Discussion Networks. Journal of Communication. № 60: 680– DOI: 10.1111/j.1460-2466. 2010.01509.x
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  • Holt, R. (2004). Dialogue on the Internet: Language, Civic Identity, and Computer Mediated Communication (Civic Discourse for the Third Millennium). Westport, CT and London: Praeger, 272.
  • Zappavigna, M. (2018). Searchable Talk: Hashtags and Social Media Meta­discourse. London: Bloomsbury, 264.
  • Peter, M. Shane (Ed.). (2004). Democracy Online: the Prospects for Political Renewal through the Internet. New York; London: Routledge, 300.
  • Warnick, B. & Heineman, D. S. (2007). Rhetoric Online: Persuasion and Politics on the World Wide Web (Frontiers in Political Communication). New York: Peter Lang Inc., 160.
  • Okulska, U. & P. Cap (Eds.). (2010). Perspectives in Politics and Discourse (Discourse Approaches to Politics, Society and Culture). Amsterdam: John Benjamins Publishing Company, DOI: https://doi.org/10.1075/dapsac36.
  • Schaeffner, C. (2009). Political Discourse, Media and Translation. Cambridge: Cambridge Scholars Publishing,
  • Digital Democracy: Discourse and Decision Making in the Information Age. (2005). By Barry N. Hague (Author, Editor), Brian D Loader (Editor). Lon­don: Routledge, 295.
  • Park, Y.-R. & Tosaka, Y. (2010). Metadata Quality Control in Digital Repo­sitories and Collections: Criteria, Semantics, and Mechanisms. Cataloging & Classification Quarterly. Volume 4. Issue 8: 696–715. DOI: 10.1080/01639374. 2010.508711.
  • Feng, J., Yuan, X., Wang, Z., Xu, H. & Yan, S. (2012). Auto-Grouped Sparse Representation for Visual Analysis. Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol. 7572. Berlin: Springer. DOI: 10. 1007/978-3-642-33718-5_46.
  • Taylor, C. & Marchi, (2018). Corpus Approaches to Discourse: A Critical Review. London/New York: Routledge, 314.
  • The Cambridge Handbook of English Corpus Linguistics. (2015). Edited by Douglas Biber and Randi Reppen. Cambridge: Cambridge University Press, 623. DOI: 1017/CBO9781139764377.
  • Baker, P. (2008). A useful methodological synergy? Combining Critical Dis­course Analysis and Corpus Linguistics to examine discourses of refugees and asylum seekers in the UK press. Discourse and Society. № 19 (3): 273– DOI: 10.1177/0957926508088962.
  • Рябченко, Н. А., Малышева, О. П., Гнедаш, А. А. (2019). Управление по­ли­тическим контентом в социальных сетях в период предвыборной кампании в эпоху постправды. Полис. Политические исследования. № 2: 92– DOI: https://doi.org/10.17976/jpps/2019.02.07. [Ryabchenko, N. A., Malysheva, O. P., Gnedash, A. A. (2019). Upravlenie politicheskim kontentom v social’nyh setjah v period predvybornoj kampanii v jepohu postpravdy. Polis. Politicheskie issledo­vanija. № 2: 92–106. DOI: https://doi.org/10.17976/ jpps/2019.02.07.]
  • Holt, R. (2004). Dialogue on the Internet: Language, Civic Identity, and Computer Mediated Communication (Civic Discourse for the Third Millennium). Westport, CT and London: Praeger, 272.
  • Ильин, В. И. (2007). Потребление как дискурс. http://www.old.jourssa.ru/ 2007/CB5/Ilyin.pdf. дата обращения 20.03.2022. [Il’in, V. I. (2007). Potreblenie kak diskurs. http://www.old.jourssa.ru/2007/CB5/Ilyin.pdf. data obrashhenija 20.03.2022.]
  • Значение слова «боты». https://kartaslov.ru/значение-слова/боты дата обра­щения 20.03.2022. [Znachenie slova «boty». https://kartaslov.ru/znachenie-slova/ boty data obrashhenija 20.03.2022.]
  • Рябченко, Н. А., Катермина, В. В., Гнедаш, А. А., Малышева, О. П. (2018). Политический контент социальных движений в online-пространстве совре­менных государств: методология анализа и исследовательская практика. Юж­но-российский журнал социальных наук. № 3: 139– [Ryabchenko, N. A., Katermina, V. V., Gnedash, A. A., Malysheva, O. P. Politicheskij kontent social’nyh dvizhenij v online-prostranstve sovremennyh gosudarstv: metodologija analiza i issledovatel’skaja praktika. Juzhno-rossijskij zhurnal social’nyh nauk. № 3: 139–162.]
  • The pragmatics of politeness. (2014). By Geoffrey Leech. (Oxford studies in sociolinguistics.) New York: Oxford University Press, 343.
  • Ильин, В. И. (2007). Потребление как дискурс. http://www.old.jourssa.ru/ 2007/CB5/Ilyin.pdf. дата обращения 20.03.2022. [Il’in, V. I. (2007). Potreb­lenie kak diskurs. http://www.old.jourssa.ru/2007/CB5/Ilyin.pdf. data obrashhenija 20.03.2022.]
  • Рябченко, Н. А., Малышева, О. П. (2020). Характеристики современной по­литической коммуникации в онлайн-пространстве. Вопросы когнитивной линг­вистики. № 2: 101– [Ryabchenko N. A., Malysheva O. P. (2020). Harakteristiki sovremennoj politicheskoj kommunikacii v onlajn-prostranstve. Voprosy kogni­tiv­noj lingvistiki. № 2: 101–113.]
  • Рябченко, Н. А., Малышева, О. П. (2019). Трансформация социально-по­литической коммуникации: структурно-реляционный анализ феномена Fake News. Материалы IX международной социологической Грушинской конфе­ренции «Социальная инженерия: как социология меняет мир», 20—21 марта 2019 г. / отв. ред. А. В. Кулешова. Москва: АО «ВЦИОМ», 46– [Ryabchenko, N.A., Malysheva, O. P. (2019). Transformacija social’no-politicheskoj kommu­nikacii: strukturno-reljacionnyj analiz fenomena Fake News. Materialy IX mezhdu­narodnoj sociologicheskoj Grushinskoj konferencii «Social’naja inzhenerija: kak sociologija menjaet mir», 20—21 marta 2019 g. / otv. red. A. V. Kuleshova. Moskva: AO «VCIOM», 46–52.]
  • Social Media Stats in United States Of America – August 2020. https:// statcounter.com/social-media-stats/all/united-states-of-america# Retrieved on 20.03.2022.
  • Donald J. Trump Twitter. (2020). https://twitter.com/realdonaldtrump. Retrieved on 20.03.2022.
  • Donald J. Trump Facebook. (2020). https://www.facebook.com/DonaldTrumpp. Retrieved on 20.03.2022.

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Ильин, В. И. (2007). Потребление как дискурс. http://www.old.jourssa.ru/ 2007/CB5/Ilyin.pdf. дата обращения 20.03.2022. [Il’in, V. I. (2007). Potreblenie kak diskurs. http://www.old.jourssa.ru/2007/CB5/Ilyin.pdf. data obrashhenija 20.03.2022.]

Карасик, В.И. (2004). Языковой круг: личность, концепты, дискурс. Москва: Гнозис, 390. [Karasik, V.I. (2004). Jazykovoj krug: lichnost’, koncepty, diskurs. Moskva: Gnozis, 390.]

Макарычева, М. Г. (2006). Дискурс как предмет изучения в сфере международной политики (пример американо-российских отношений). Вестник Ниже­го­родского университета им. Н.И.Лобачевского. Серия: Социальные науки. 1: 567–574. [Makarycheva, M. G. (2006). Diskurs kak predmet izuchenija v sfere mezhdunarodnoj politiki (primer amerikano-rossijskih otnoshenij). Vestnik Nizhe­gorodskogo universiteta im. N.I.Lobachevskogo. Serija: Social’nye nauki. 1: 567–574.]

Ревзина, О. Г. (2005). Дискурс и дискурсивные формации. Критика и семиотика. Новосибирск, Вып. 8: 66–78. [Revzina, O. G. (2005). Diskurs i diskursivnye formacii. Kritika i semiotika. Novosibirsk. № 8: 66–78.]

Рябченко, Н. А., Малышева, О. П. (2020). Характеристики современной полити­ческой коммуникации в онлайн-пространстве. Вопросы когнитивной линг­вис­тики. № 2: 101–113. [Ryabchenko N. A., Malysheva O. P. (2020). Harakte­ristiki sovremennoj politicheskoj kommunikacii v onlajn-prostranstve. Voprosy kognitivnoj lingvistiki. № 2: 101–113.]

Рябченко, Н. А., Малышева, О. П., Гнедаш, А. А. (2019). Управление поли­ти­ческим контентом в социальных сетях в период предвыборной кампании в эпоху постправды. Полис. Политические исследования. № 2: 92–106. DOI: https://doi.org/10.17976/jpps/2019.02.07. [Ryabchenko, N. A., Malysheva, O. P., Gnedash, A. A. (2019). Upravlenie politicheskim kontentom v social’nyh setjah v period predvybornoj kampanii v jepohu postpravdy. Polis. Politicheskie issledo­vanija. № 2: 92–106. DOI: https://doi.org/10.17976/jpps/2019.02.07.]

Рябченко, Н.А., Малышева, О.П. (2019). Трансформация социально-политической коммуникации: структурно-реляционный анализ феномена Fake News. Материалы IX международной социологической Грушинской конференции «Социальная инженерия: как социология меняет мир», 20—21 марта 2019 г. / отв. ред. А. В. Кулешова. Москва: АО «ВЦИОМ», 46–52. [Ryab­chenko, N. A., Malysheva, O. P. (2019). Transformacija social’no-politicheskoj kommu­nikacii: strukturno-reljacionnyj analiz fenomena Fake News. Materialy IX mezhdunarodnoj sociologicheskoj Grushinskoj konferencii «Social’naja inzhe­nerija: kak sociologija menjaet mir», 20—21 marta 2019 g. / otv. red. A. V. Kuleshova. Moskva: AO «VCIOM», 46–52.]

Рябченко, Н. А., Катермина, В. В., Гнедаш, А. А., Малышева, О. П. (2018). По­литический контент социальных движений в online-пространстве совре­менных государств: методология анализа и исследовательская практика. Южно-российский журнал социальных наук. № 3: 139–162. [Ryabchenko, N. A., Katermina, V.  V., Gnedash, A.A., Malysheva, O. P. Politicheskij kontent so­cial’nyh dvizhenij v online-prostranstve sovremennyh gosudarstv: metodo­logija analiza i issledovatel’skaja praktika. Juzhno-rossijskij zhurnal social’nyh nauk. № 3: 139–162.]

Убийко, В. И., Батталова, А. Р. (2012). О понятии дискурсивного поля. Прио­ритеты современной русистики в осмыслении языкового пространства. Уфа, 179–184. [Ubijko, V. I., Battalova, A. R. (2012). O ponjatii diskursivnogo polja. Prioritety sovremennoj rusistiki v osmyslenii jazykovogo prostranstva. Ufa, 179–184.]

Baker, P. (2008). A useful methodological synergy? Combining Critical Discourse Analysis and Corpus Linguistics to examine discourses of refugees and asylum seekers in the UK press. Discourse and Society. № 19 (3): 273–306. DOI: 10. 1177/0957926508088962.

Brundidge, J. (2010). Encountering «Difference» in the Contemporary Public Sphere: The Contribution of the Internet to the Heterogeneity of Political Discussion Networks. Journal of Communication. № 60: 680-700. DOI: 10.1111/j.1460-2466.2010.01509.x.

Davis, R. (1999). The Web of Politics: The Internet’s Impact on the American Political System. Oxford: Oxford University Press, 248.

Diamond, I. & Quinby, L. (1988). Feminism & Foucault: Reflections on Resistance. Boston: Northeastern University Press.

Digital Democracy: Discourse and Decision Making in the Information Age. (2005). By Barry N. Hague (Author, Editor), Brian D Loader (Editor). London: Routledge.

Feng, J., Yuan, X., Wang, Z., Xu, H. & Yan, S. (2012). Auto-Grouped Sparse Representation for Visual Analysis. Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol. 7572. Berlin: Springer. DOI: 10.1007/978-3-642-33718-5_46.

Foucault, M. (1981). The Order of Discourse. Untying the Text: A Post–structuralist Reader. (Ed. Robert Young). Boston: Routledge & Kegan Paul, 48–78.

Holt, R. (2004). Dialogue on the Internet: Language, Civic Identity, and Computer Mediated Communication (Civic Discourse for the Third Millennium). Westport, CT and London: Praeger.

Katermina V. & Gnedash A. (2020) Linguistic models of social and political commu­nication in the online-space: cognitive and pragmatic aspect. SHS Web of Con­ferences. 88(2): 01003. DOI:10.1051/shsconf/20208801003.

Maglione, G. (2014). Discursive fields and subject positions: becoming ‘victim’, ‘offender’ and ‘community’ in restorative justice. Restorative Justice. Vol. 2. Issue 3: 327–348. DOI: 10.5235/20504721.2.3.327.

Okulska, U. & P. Cap, P. (Eds.). (2010). Perspectives in Politics and Discourse (Discourse Approaches to Politics, Society and Culture). Amsterdam: John Benjamins Publishing Company. DOI: https://doi.org/10.1075/dapsac.36.

Park, Y.-R. & Tosaka, Y. (2010). Metadata Quality Control in Digital Repositories and Collections: Criteria, Semantics, and Mechanisms. Cataloging & Classification Quarterly. Volume 4. Issue 8: 696-715 DOI: 10.1080/01639374.2010.508711.

Peter, M. Shane (ed.). (2004). Democracy Online: the Prospects for Political Renewal through the Internet. New York; London: Routledge.

Schaeffner, C. (2009). Political Discourse, Media and Translation. Cambridge: Cam­bridge Scholars Publishing.

Snow, D. (2013). Discursive Fields. The Wiley-Blackwell Encyclopedia of Social and Political Movements. Oxford: Blackwell Publishing Ltd. DOI: 10.1002/ 9780470674871.wbespm072.

Weedon, C. (1987). Feminist Practice and Poststructuralist Theory. Oxford: Blackwell Publishing.

Taylor, C. & Marchi, A. (2018). Corpus Approaches to Discourse: A Critical Review. London/New York: Routledge.

The Cambridge Handbook of English Corpus Linguistics. (2015). Edited by Douglas Biber and Randi Reppen. Cambridge: Cambridge University Press, 623. DOI: 10.1017/CBO9781139764377

The pragmatics of politeness. (2014). By Geoffrey Leech. (Oxford studies in socio­linguistics.) New York: Oxford University Press.

Warnick, B. & Heineman, D. S. (2007). Rhetoric Online: Persuasion and Politics on the World Wide Web (Frontiers in Political Communication). New York: Peter Lang Inc., 160.

Zappavigna, M. (2018). Searchable Talk: Hashtags and Social Media Metadiscourse. London: Bloomsbury.

 

Sources

Значение слова «боты». https://kartaslov.ru/значение-слова/боты дата обращения 20.03.2022. [Znachenie slova «boty». https://kartaslov.ru/znachenie-slova/boty data obrashhenija 20.03.2022.]

Donald J. Trump Twitter. (2020). https://twitter.com/realdonaldtrump. Retrieved on 20.03.2022.

Donald J. Trump Facebook. (2020). https://www.facebook.com/DonaldTrump.p.  Retrieved on 20.03.2022.

Social Media Stats in United States Of America – August 2020. https://gs. statcounter.com/social-media-stats/all/united-states-of-america# Retrieved on 20.03.2022.

Manuscript was submitted: 20.03.2022.

Double Blind Peer Reviews: from 29.07.2022 till 05.08.2022.

Accepted: 07.08.2022.

Брой 53 на сп. „Реторика и комуникации“, октомври 2022 г. се издава с финансовата по­мощ на Фонд научни изследвания, договор № КП-06-НП3/75 от 18 декември 2021 г.

Issue 53 of the Rhetoric and Communications Journal (October 2022) is published with the financial support of the Scientific Research Fund, Contract No. KP-06-NP3/75 of December 18, 2021.