Social Network VKontakte Research Articles

DETECTION OF DEPRESSION AMONG SOCIAL NETWORK USERS USING MACHINE LEARNING METHODS

Statistical data provided by the FSBI “NMITSPN named after V.P. Serbsky” of the Ministry of Health of Russia indicate that depression, as a psychoemotional state, is the main cause of concern around the world, which in most cases leads to suicide, if not detected, and to a threat to others. Studies show that depression tends to have an impact on writing style and appropriate language use. The main purpose of the proposed study is to study user messages on the VKontakte social network and identify attributes that may indicate depressive symptoms of users. The article uses machine learning approaches (logistic regression, random forest, support vector machine, XGBoost) and natural language processing methods (removal of stop words, character deletion, tokenization, lemmatization) to prepare data and evaluate their effectiveness. The work demonstrated that the ability to search for depressed users with an accuracy of 77% using the XGBoost classifier. This method is combined with other linguistic functions (N-gram + TF-IDF) and LDA to achieve higher accuracy. In conclusion, the main conclusions of the study are formulated.

GEOINFORMATION SYSTEMS IN THE STUDY OF MUSLIM HOLY SITES IN THE SOUTHERN URALS

In the recent decade, there has been a tendency in the Southern Urals to reconstruct old holy places and to form new ones revered by the Muslims. To date, more than 115 such objects have been recorded. Most of them are revered only by residents of small villages, but some have become important for the Muslims throughout the region.
 
 The purpose of the study is to determine the most popular places of worship for the Muslims in the Southern Urals. Considering that some of the objects of regional importance have been revered by the Muslims since the XIV century, and others, having appeared relatively recently, quickly became one of the most visited places, it is necessary to determine the factors influencing the growth of this popularity.
 
 Historiographical base of the study. The research team analyzed the works of domestic and foreign authors, which mainly describe the conditions of the studied objects, religious rituals, organized events, etc. In addition, it was possible to attract the experience of researchers in implementing projects in geoinformation systems in religious studies.
 
 Materials and methods of the study. Geoinformation methods were used for the study. Based on archival materials, developments of archaeologists, ethnographers, theologians, culture experts, materials of Internet communities and other open sources, as well as the free cross-platform geoinformation system Quantum GIS, we have created the following layers: 1) mausoleums; 2) places of worship according to the data given by Russia’s Central Spiritual Governance for the Muslims; 3) places of worship according to the Internet community in the social network VKontakte Sufi RB; 4) religious tourist routes.
 
 Study results. Thanks to the use of geoinformation systems, it was possible to identify the most popular holy places among the Muslims of the Southern Urals. It is established that the most actively visited objects are the mausoleum of Hussein Bey in Chishminsky district of the Republic of Bashkortostan and the Ilchigulovo-IV burial mound (the sacred mountain Narystau) in Miyakinsky district of the Republic of Bashkortostan.
 
 Conclusions. The results of the study indicate that the popularity of objects is influenced by a number of factors, among which the presence of a monumental structure, representation of the object in the media space and the Internet, as well as availability of objective scientific data about it are of key importance.

Modeling Seasonality of Emotional Tension in Social Media

Social media has become an almost unlimited resource for studying social processes. Seasonality is a phenomenon that significantly affects many physical and mental states. Modeling collective emotional seasonal changes is a challenging task for the technical, social, and humanities sciences. This is due to the laboriousness and complexity of obtaining a sufficient amount of data, processing and evaluating them, and presenting the results. At the same time, understanding the annual dynamics of collective sentiment provides us with important insights into collective behavior, especially in various crises or disasters. In our study, we propose a scheme for identifying and evaluating signs of the seasonal rise and fall of emotional tension based on social media texts. The analysis is based on Russian-language comments in VKontakte social network communities devoted to city news and the events of a small town in the Nizhny Novgorod region, Russia. Workflow steps include a statistical method for categorizing data, exploratory analysis to identify common patterns, data aggregation for modeling seasonal changes, the identification of typical data properties through clustering, and the formulation and validation of seasonality criteria. As a result of seasonality modeling, it is shown that the calendar seasonal model corresponds to the data, and the dynamics of emotional tension correlate with the seasons. The proposed methodology is useful for a wide range of social practice issues, such as monitoring public opinion or assessing irregular shifts in mass emotions.

Bots as an online communication tool in the political process on the example of the EDC-2023 in Russia

The article presents a brief theoretical overview of the types of bots, the purposes of their use, identification methods, and also examines global political campaigns with the active use of automated systems. Using the example of the 2023 regional election campaign, an empirical study was conducted, the purpose of which is to identify the features of using bot systems in online communication of the authorities represented by candidates and society. The study was conducted on the basis of open data from the VKontakte social network - all publications were selected in which candidates for the election of heads of regions for the period from August 28 to September 10, 2023 were mentioned. A total of 21 regions and 116 candidates were analyzed. The largest number of bots is typical for Moscow, the Moscow Region, the Republic of Khakassia, and the Pskov region. The detection of bots was based on the method of static signs: the same / similar text of the post, the absence of user activity, the absence of real photos, the lack of a personal profile. The data obtained allowed us to formulate conclusions about the use of bots to create a positive and neutral agenda. At the same time, the publications created automatically had zero or low views, which indicates their invisibility to the final audience and inability to influence the formation of public opinion. However, data on the mention of candidates and the presence of their positive image in the social network can be used in the preparation of administrative or analytical reports.

СОЦИАЛЬНЫЕ СЕТИ В УЧЕБНОЙ И СОЦИАЛЬНО-КУЛЬТУРНОЙ КОММУНИКАЦИИ ИНОСТРАННЫХ СТУДЕНТОВ-НЕФИЛОЛОГОВ

The article deals with the role and functions of an account in the social network Vkontakte, created for foreign non-philological faculties students. It helps to expand their communication space in Russian, to develop language, speech and cultural competencies and emotional involvement in cultural and historical events of our country, city and university. The online community facilitates to form the international collective and creates additional motivations to communicate in Russian and study in Russian universities. All that pictures an attractive image of a foreign university student in the digital environment.

The discourse of right-wing radicals in online communities (using the example of the VK social network)

The object of the study are discourses that are broadcast by supporters of right-wing radical ideology in the relevant communities in the largest Russian social network VKontakte. The study of the discourse of right-wing radicals is an urgent topic of scientific and practical research in modern socio-political conditions. The study of the discourses of right-wing radicals helps to better understand the value-ideological attitudes of representatives of this group of political extremists and to reveal the rhetorical techniques and practices that they exploit in the process of mobilizing supporters. The analysis of the discourses of right-wing radicals in social media is especially relevant, since in the conditions of a digital society, the Internet is becoming the leading source of information for a significant part of the population. Four thematic types of discourses are identified – racist, anti-Soviet, anti-Semitic and social-populist discourses. Racist discourse is characteristic of the communities of the "new" and "alternative" right, while supporters of the "old", imperial right actively exploit the theme of anti-Soviet discourse. Anti-Semitic and social-populist discourses are rather poorly represented in the communities selected for the study. With the help of PolyAnalist software, six clusters of terms have been identified based on the occurrence in texts in right-wing radical communities: news, value, anti-migrant, historical, socio-economic. The analysis of the frequency of mentioning words in negative tonality and positive tonality in the posts of right-wing communities is carried out. There are two major objects to which the negative discourse is directed: the modern political regime in the Russian Federation and the Soviet period.

Assessment of a Person's Social Success Through the Characteristics of Interpersonal Relationships in A Virtual Environment

The sudden spread of the COVID-19 pandemic has convincingly proved the role of social networks in human life activity as an actor of interpersonal relations. The need for isolation and the limitation of face-to-face communication between people has significantly transformed the system of interpersonal connections through an extensive increase in social contacts in the virtual environment and the growing importance of online social services. In the study, we addressed the problem of diagnosing the indicator of social success of a personality, reflecting characteristics of its interpersonal relations in offline activity through the attributes of its virtual activity in social networks. The research was based on the methods of social network analysis and traditional ways of psychodiagnostics. Social networks were analyzed using social graphs - mathematical models that describe the characteristics of relationships between users in social networks through various metrics (friends, elements, edges, density, closeness centrality, degree centrality, clustering coefficient, etc.). The study aims to prove the predictive validity of social graph indicators as predictors of personality social success through correlations of graph characteristics reflecting the features of interpersonal relations of a social network user in a virtual environment with the socio-psychological hands of traditional psychodiagnostic tools. The study included 601 subjects. Data was collected using psychological questionnaires and personal profiles from the social network VKontakte. The results of the study showed correlations between several characteristics of social graphs (density, clustering coefficient, closeness centrality, etc.) and several indicators of psychodiagnostic techniques (Dominance index in relationships with others, Organizational abilities, Desire for people, Narcissism, etc.). The significant contribution of the research is to expand the methodological apparatus of psychological science and to open new methods of predicting interpersonal relations of personality through its activity in a virtual environment.

Library in the Space of Books and Events of the 36th Moscow International Book Fair

From August 30 to September 3, 2023, the 36th Moscow International Book Fair was held at the Expo Centre Central Exhibition Complex on Krasnopresnenskaya Embankment. The Forum brought together participants from various regions of Russia, as well as representatives of 21 countries, including seven countries of the Commonwealth of Independent States, Germany, China and Iran, and was attended by more than 34 thousand people. The VKontakte social network has become a partner of online broadcasts and the official MIBF network. More than 300 events were held at seven venues. Publishers from Moscow, St. Petersburg and 26 other Russian regions presented their books.The article presents an annual review of the activities of the MIBF: The National competition “Book of the Year — 2023”, the competition of the Russian Library Association “The Best professional Book of the Year”, the 3rd Readers’ Assembly of the Commonwealth, the industry conference “Book Market of Russia — 2023”, the Moscow International Children’s Book Fair. In the event context of the most relevant events for the library industry, libraries’ understanding of their role in the socio-cultural life of society is revealed: achievements and initiatives aimed at preserving heritage, promoting books and reading are noted, new projects, including book publishing, are presented, the results of the participation of the Russian State Library and the publishing house “Pashkov Dom” in the MIBF-2023 are summed up.

Possibilities of Predicting a Person's Substance Use Behaviour and Mental Health Through Social Media in a COVID-19 Crisis Context

The negative psychological consequences of the COVID-19 pandemic and the forced isolation of a large proportion of people worldwide have demonstrated the need to develop ways and technologies to reduce the effects of sudden threats of this type. The basis of any practical work to minimize the negative psychological consequences of the COVID-19 pandemic associated with substance use is the monitoring and diagnosis of the psychological resources of the individual. The article aims to show the possibilities of predicting the behavior of an individual through the content analysis of posts and reposts of their profile on the social network VKontakte on the example of the propensity to use psychoactive substances and to substantiate the possibilities of optimizing and automating such prediction through the use of category markers. Content analysis was carried out by latent semantic analysis of texts extracted from posts and reposts of VKontakte social network users with subsequent content analysis through selecting markers - category words. As a result, a categorical grid was built, which increases the efficiency of content analysis of posts and reposts of users and is suitable for further automation of such research by machine learning methods.

Online Education after the Pandemic: Student Problems and Opportunities Research Using Big Data Tools

This paper presents a scientifically based approach to analyzing large volumes of data from digital traces of students on social networks, which allows you to effectively identify emerging and most discussed problems among students, as well as highlight pain points that provide opportunities for growth, development of universities and improvement of the characteristics of the educational process, support for students etc. The study is based on a thematic analysis of messages published in university communities on the VKontakte social network using big data tools. The study results showed that Russian university students still face a number of challenges, including weak technical infrastructure at universities, a digital divide in access to online education, and negative attitudes towards distance learning.The scientific problem of the study is the contradiction between the existing volume of unstructured data of students’ digital traces in social networks and the lack of a scientifically-based and proven methodological approach to the analysis and evaluation of this voluminous data, which creates obstacles to fundamental research into the relationship between students’ activity in social networks and their satisfaction quality of the educational process. The practical focus is determined in conducting data analysis using big data tools. The findings and evidence-based implications are useful for developing innovative strategies and tools for assessing and supporting students.The results show that the use of big data tools for tracking trends based on digital traces of students on social networks provides highly accurate analytical data and can become the basis for identifying problematic situations in individual universities and the industry as a whole, for data-driven decision-making and management .

Диалог исполнительной власти и общества в социальной сети «ВКонтакте»

The aim of the study is to identify and describe the communication strategies and tactics that are used by the Russian executive authorities in a digital dialogue with citizens. The scientific novelty of the study is accounted for by the elaboration of the “digital discourse of executive authorities” notion and the description of the communication strategies and tactics implemented in this discourse. Based on a new material, i.e., a sample of posts of the Ministry of Education and Science of the Perm Territory posted on the VKontakte social network, an analysis of typical strategies and tactics aimed at forming a positive image of the executive authorities is carried out. It is shown that the speech strategy of self-presentation is the main one in the media communication of the authorities with citizens. This strategy receives concrete expression in the tactics of analysis-plus, presentation of an event, self-justification. By involving Engagement Rate by Reach, the pragmatic effect of speech influence on the audience is revealed. As a result, it is proved that the effectiveness of the dialogue between the executive authorities and society is due to the influence of the following discourse factors: coinciding presuppositions of the addressee and the addresser, due regard for the needs of the target audience in the content, reflection of significant social life events in the published materials.

Profiles in the social network of university students as a source of migration data (on the example of RUDN University)

The subject of the research is the territorial origin of students and graduates of the Patrice Lumumba Peoples' Friendship University of Russia. The purpose of the work implies the study of the internal Russian educational migration of persons who entered the university in question at different times. The research method was the analysis of digital traces of users of the VKontakte social network. The information basis of the work covered data on the regions of origin indicated in user profiles. It is indicated that the study of educational and youth migration based on social network data is becoming widespread in Russian science. Migration processes in specific regions of the country or the participation of certain universities in the organization of educational mobility are investigated. The novelty of the work is associated with the use of an unconventional source of information about migration processes. It is shown that, despite the positioning of the RUDN as an internationally oriented university, the students of the university are mainly Russian citizens. As a result of studying the profiles of VKontakte users, it was revealed that most of those who indicated RUDN as a place of study are natives of Moscow. Among the persons who marked other regions of the Russian Federation as origin in VKontakte profiles the largest number of students and graduates of RUDN are from Chukotka and Yamalo-Nenets Autonomous Okrugs, the Moscow and Magadan regions, several national-territorial subjects of the Russian Federation. The main factors determining the origin of educational migrants' are the territorial accessibility of the university city and the insufficient level of higher school development in the regions of educational migrants.

Web-ресурстардағы экстремисттік мәліметтерді анықтауға арналған машиналық әдістерді оқыту және сынау үшін қазақ тіліндегі мәтіндер корпусын құру

The article deals with the issue of creating a corpus in the Kazakh language, which is necessary to create a semantic model for identifying extremist information on web resources. As a result of the study, text data was collected from closed groups of the VKontakte social network, YouTube comments and news sites using previously developed analytics. The texts were classified into two classes: «extremist» and «neutral». A corpus for further training of models was created. The total number of extremist messages included in the corpus is about 1,200, and the total number of words in the corpus is about 140,000 words. The distribution of extremist and neutral texts was carried out, the analysis of the corpus was carried out using a word cloud. The Python 3.7 programming language with pandas, numpy, matplotlib, plotly, bokeh, cufflinks, spacy, googletrans packages was used as the main calculation and visualization libraries for data analysis. Signs of texts of extremist content were identified.

Comparative analysis of social portraits of top female and male bloggers (On the example of VKontakte social network)

Comparative analysis of social portraits of top female and male bloggers (On the example of VKontakte social network)

Эффективное ведение сообщества вуза в социальных сетях

The study authors conducted a comparative analysis of the content published by university SMM specialists who entered the top 5 of the M-RATE ranking for social media work in 2022. The aim of the study was to identify correlations between the form of posts in university communities and participant activity, as well as between content and post visibility. The study confirmed hypotheses that user activity on VKontakte when interacting with textual publications is lower than when interacting with visual content, and that the VKontakte social network shows users entertaining publications more often than useful or PR posts. Based on the analysis of 7163 posts published from January 1, 2022, to December 31, 2022, the authors provide recommendations for universities to improve their social media work effectiveness.

Analysis of Interpersonal Relationships of Social Network Users Using Explainable Artificial Intelligence Methods

The emergence of the social networking phenomenon and the sudden spread of the coronavirus pandemic (COVID-19) around the world have significantly affected the transformation of the system of interpersonal relations, partly shifting them towards virtual reality. Online social networks have greatly expanded the boundaries of human interpersonal interaction and initiated processes of integration of different cultures. As a result, research into the possibilities of predicting human behavior through the characteristics of virtual communication in social networks has become more relevant. The aim of the study is: to explore the possibilities of machine learning model interpretability methods for interpreting the success of social network users based on their profile data. This paper uses a specific method of explainable artificial intelligence, SHAP (SHapley Additive exPlanations), to analyze and interpret trained machine learning models. The research is based on Social Network Analysis (SNA), a modern line of research conducted to understand different aspects of the social network as a whole as well as its individual nodes (users). User accounts on social networks provide detailed information that characterizes a user's personality, interests, and hobbies and reflects their current status. Characteristics of a personal profile also make it possible to identify social graphs - mathematical models reflecting the characteristics of interpersonal relationships of social network users. An important tool for social network analysis is various machine learning algorithms that make different predictions based on sets of characteristics (social network data). However, most of today's powerful machine learning methods are "black boxes," and therefore the challenge of interpreting and explaining their results arises. The study trained RandomForestClassifier and XGBClassifier models and showed the nature and degree of influence of the personal profile metrics of VKontakte social network users and indicators of their interpersonal relationship characteristics (graph metrics).

Low Civility and High Incivility in Russian Online Deliberation

One of the most studied fields in deliberative research is (in)civility in Internet-based political discussions on issues of common concern. Uncivil behaviour demonstrated by participants in online communication has various forms and negative effects on the process and outcomes of e-deliberation as well as on deliberators’ reactions and attitudes, which have been predominantly investigated in Western democracies. However, this issue has been poorly covered in the countries with less stable democratic traditions and values. This paper explores speech culture with a focus on civility and incivility in Russian political conversations conducted on Russian social media. The authors analyse mass-scale web political discussions on a polarising issue of the court sentence of the politician Alexei Navalny (2021), taking one of the most popular Russian social networks VKontakte. For this study, scholars use discourse analysis based on the works on deliberative democracy proposed by J. Habermas (1996). They conclude that Russian political speech regarding Navalny’s sentence and conducted on the VKontakte social media platform can be characterised by a great extent of uncivil speech unbalanced by a low extent of civil speech. The conversations are not oriented towards mutual recognition or reaching a consensus, as participants are often distracted from the main issue being discussed, and turn to interpersonal topics instead.

Social networks as a tool for involving citizens in governance (using the example of the official pages of local administrations of cities in Central Russia)

Social networks are considered by the population as the leading way of communication, therefore public authorities use new technologies to inform and involve citizens in management. The purpose of the work is to evaluate the interaction of citizens and authorities using the official pages of local administrations on the VKontakte social network. The subject of the study is social networks as a tool of interaction between authorities and citizens. The article analyzes the official pages (state publications) of 7 large cities, which are the administrative centers of the subjects of the Central Federal District. Main research methods: analysis, comparison, generalization, methods of descriptive statistics. The author examines the work of Russian and foreign researchers on the problem of the use of social networks in the activities of public authorities. The indicators reflecting the effectiveness of official communities were studied: the activity of citizens on the pages, the level of involvement, the publication activity of the administration, content formats. The data was collected and processed manually and with the help of specialized services. There are few studies in which a comparative analysis of the interaction of local governments with citizens in social networks is carried out, so the article complements the current literature on digital communications in the public sector. An analysis of the indicators that characterize the activity and involvement on the official pages of local administrations shows that the practice of using social networks in the regions is different. All official pages are active, page owners (local administrations) and subscribers (citizens) are looking for ways to interact online. The results will help to identify the strengths and weaknesses of the official pages and offer recommendations for improving the participation of citizens in the management process through social networks.

Армянская диаспора в социальных сетях

Currently, there is a massive involvement of migrants in social networks, in which virtual communication becomes the main form of their social interaction both within their ethnic diaspora and with other diasporic and host online communities. The problematic situation is connected with the fact that social networks have become an almost uncontrolled Internet resource with unlimited possibilities for the dissemination of information and a variety of forms of interaction, including verbal aggression, in- tolerance towards the interlocutor, ignoring ethical norms of communication, etc. The purpose of the scientific article is to identify the predominant type of social interaction of the Armenian diaspora in Russian-speaking social networks with relatively new ethnic diasporas (Tajik and Kyrgyz) and the host community. The analysis is based on the results of a sociological study "Social interaction of Diasporas in social networks" conducted by three methods of collecting empirical information (online questionnaire survey of representatives of the Armenian, Tajik and Kyrgyz diasporas in Russia; in- depth interview with representatives of diasporas; qualitative analysis of information posts of representatives of diasporas in the social network Vkontakte). The study re- vealed that the predominant type of social interaction of representatives of the Arme- nian Diaspora with the diasporas of Tajikistan and Kyrgyzstan, as well as with the host community, is conflict. The main causes of the conflict type of interaction of repre- sentatives of the Armenian Diaspora with representatives of Tajikistan and Kyrgyz- stan, as well as with the host community, were revealed. Acknowledgment: The study was carried out with the financial support of the RFBR for the implementation of the scientific Project №. 20-511-05007 “Socio-economic potential of the Armenian Diaspora in the context of integration processes in the EAEU”. Contract № 20-511-05007/21.

Situational and communication content in the framework of SMM brand promotion in social networks

In the article the authors present the study results of the content use in SMM promotion. Information is given about the types of content, situational and communication contents are considered. The main research method chosen for studying the designated research problem is described – content analysis, as well as its types – non-quantitative and quantitative. Non-quantitative implies the presence of a certain indicator without the use of quantitative metrics, quantitative indicates the frequency identification of quantitative indicators occurrence. The study touched upon the content consideration in the VKontakte social network of three brands: “R. O. C. S. Smart Oral Care” – a brand of oral care products, “Danone Russia” – a Russian division brand of the Danone corporation, “Respect” – a Russian trade-mark, a multi-brand chain of stores for men’s and women’s shoes, accessories. The selection criteria for the study were: community belonging to the Russian brand; daily maintenance of content; the number of subscribers from 30 to 70 thousand. The analysis is accompanied by graphs comparing reactions to situational and communication posts of the above-mentioned brands. At the work conclusion, recommendations are given for SMM managers involved in the preparation of content plans, brands’ content strategies. The authors formulate conclusions about what needs to be done for the most effective promotion in social networks.