Digital Transformation and Global Society

This paper presents the starting point to adverse event reporting and learning systems designed to describe and prevent unfavorable happenings in Public Services organizations. To achieve this goal, the Eindhoven’s Classification Method was changed to house such incidents. On the other hand, the evolutionary process of the knowledge body of such systems is to be understood as a process of energy devaluation, i.e., their data/information/knowledge will be represented and handled as pure energy transactions, being such procedures and the respective outcomes object of formal proof under a Proof Theoretical approach to Problem Solving.

The authors argue that linking in time and space data on search queries and discussions in social networks with remote sensing data and municipal statistics provides valuable information about actual problems in urban agglomeration development. Examples of models for an analysis of such information and model-aided results related to recreational needs and environment pollution confirmed usefulness of linking data for evidence-based policy making in agglomeration development. As a result of the implementation of the models, three goals were achieved: the working ability of the general approach for environmental assessment of agglomerations was tested; new informative results were obtained, characterizing the dynamics of ecological-economic objects of the coastal areas of the Gulf of Finland and the relationship between indicators of the state of these objects; new approaches to integrating data that were not previously used together (remote sensing data, official data from municipal statistics, environmental monitoring data, as well as online population activity data) were tested.

The aim of this paper is to investigate the key characteristics that refrain Estonian youngsters from using public e-services and to study whether the level of engagement could be increased by redesigning the current e-service user experience. The technology acceptance model and its derivatives are used as background theoretical concept in combination with the design sprint tool for introducing an improved proactive and engaging e-service. We conduct an empirical study on the basis of interviews and a workshop with the target group. A prototype of an improved e-service is developed and the results of its validation are presented. The research outcomes point to the increasing importance of proactive delivery of public e-services. Moreover, we suggest to consider the benefits of implementing communication bridges between youngsters and governments via online channels.

The formation of the digital government nowadays belongs to the main directions of reforming public policy and governance. In Russia, the Federal Target Program “Digital Economy” is planned to be implemented with a conjugate transition from electronic to digital government. At the heart of the formation of the digital government is the idea of the state as a platform that allows to effectively implement state functions and services on a new technological basis. The technocratic approach that dominates this idea is accompanied by the conviction that effective public policy and governance is possible almost without a person and public relations. The paper aims to critically analyze the technocratic cultural values of the state as a platform. Adequate answers to the political challenges of the digital government (values of control, centralization, excessive governability, etc.) are possible when integrating a new culture of political opportunities for co-production and the emerging system of state governability through cooperation.

Government digitalization is becoming a mainstream of governance reforms with high expectations in terms of improving public value delivered by governments, raising both efficiency and effectiveness of public administration. Based on cross-country data, this paper presents a quantitative analysis of correlation between government digitalization on the one hand (based on UN E-government and E-participation indices and OECD statistics) and quality of public administration (measured by the WB’s governance indicators, Doing Business and WEF data) on the other. The results suggest that while there is statistically significant positive correlation between government digitalization and public administration performance, this relationship is stronger for government effectiveness, control of corruption, and doing business and weaker for e-participation, voice and accountability and efficiency of public spending. The findings suggest that there is direct cause and effect relationship between e-government development index and Doing Business measures. At the same time, surprisingly no direct cause and effect relationship was found between government digitalization and other governance indicators included in this study, such as government effectiveness and control of corruption. Thus, the benefits of businesses from government digitalization so far seem to be more evident than the gains of other beneficiaries. The paper concludes with analysis of some policy implications and recommendations on the directions of further research.

In this paper, we present a project on the analysis of an extensive corpus of strategic planning documents, devoted to various aspects of the development of Russian regions. The main purposes of the project are: (1) to extract different aspects of goal setting and planning, (2) to form an ontology of goals and criteria of achieving these goals, (3) to measure the similarity between goals declared by federal and municipal subjects.Such unsupervised Natural Language Processing (NLP) methods as phrase chunking, word embeddings, and latent topic modeling are used for information extraction and ontology construction as well as similarity computation. The resulting ontology should serve in short-term as a helper tool for writing strategic planning documents and in long-term resolve the need to compose strategic planning documents completely by navigating through the ontology and selecting relevant goals and criteria. The resulting similarity measure between federal and municipal goals will serve as a navigation tool for further analysis.

Digital transformation poses an intrinsic challenge for regulators. With the rapid development of digital technology, there is a need for new regulatory tools. Russia, like many other countries, is trying to improve its regulatory policy for digitalization and digital transformation. This paper aims comparing the possibilities and prospects for the use of regulatory sandboxes in developed countries, in Russia and in the EAEU.The analysis demonstrates that the transition from experimental legislation to regulatory sandboxes is associated with the need for rapid adaptation of regulation to digitalization conditions. Moreover, in Russia, the full implementation of regulatory sandboxes for digital transformation is complicated by the peculiarities of the legal system and the role of the law as the main regulatory tool. In the EAEU, the introduction of regulatory sandboxes is complicated by the different approaches of states to protecting their digital sovereignty and attitudes towards virtual jurisdictions.Based on the analysis, the new regulatory specific mechanism for the relationship between strategic planning, legal forecasting, experimentation and the formation of proactive regulation is proposed. Implementation of these recommendations would help improve legal regulation of the development of digital technologies in Russia and the EAEU. It has been demonstrated that regulatory sandboxes are one early step in a new smart—digitized and datafied—process regulatory systems.

The e-Oman Integration Platform is a data hub that enables data exchanges across government in response to transactions. With millions of transactions weekly, and thereby data exchanges, we propose to investigate the potential of gathering intelligence from these linked sources to help government officials make more informed decisions. A key feature of this data is the richness and accuracy, which increases the value of the learning outcome when augmented by other big and open data sources. We consider a high-level framework within a government context, taking into account issues related to the definition of public policies, data privacy, and the potential benefits to society. A preliminary, qualitative validation of the framework in the context of e-Oman is presented. This paper lays out foundational work into an ongoing research to implement government decision-making based on big data.

This paper presents the results of survey regarding Saint Petersburg citizens’ trust in information technologies. The research was conducted on the base of Actor-network theory ideas and Social Construction of Technology (SCOT) approach. 600 respondents participated in the survey (sampling error does not exceed 4%, 95% level of confidence). The research proposed suggests an approach for studying cybersocial trust in the sphere of G2C communications found in e-government development, online services provision, e-participation in city management. The questionnaire contained the parameters for evaluation trust in new technologies used to communicate with government representatives and get public services, to solve urban problems, and to participate in city management. The survey results indicated a high level of Internet usage, as well as an increased level of trust in financial transactions through the Internet. The level of citizens’ trust in getting public services online reached 45%, submitting e-applications – 41%, working with e-petitions – 38%, communicating with authorities via social networks – 15%. According to our research results, St. Petersburg citizens consider personal visit to public authorities as a more effective way to solve urban issues (19%), while the percentage of citizens who believe in the effectiveness of the Internet portals remain insignificant (5%).

The question if the Open Government Partnership (OGP), launched in 2011, has any impact on policies and institutions in its member-states, remains open. Despite several case studies revealing modest achievements of OGP to improve governance, little research has been done so far to explore this puzzle in general, using statistical means. Addressing this gap, this pilot study analyzes the impact of OGP membership on the quality of governance. Using policy feedback theory and Bayesian Structural Equation Modeling (BSEM), we have discovered that OGP might have an indirect influence on the governance quality via the development of civic participation, government transparency and feedback mechanisms.

The purpose of this study was to identify the Internet potential in intersectoral interaction (authorities, non-profit organizations and population) in the Yaroslavl region. In this study, the materials of the quantitative survey conducted in January-February 2018 in the territory of the Yaroslavl region were used in comparison with the data of regular all-Russian studies. In addition, materials from two expert interviews (among representatives of regional and local authorities and among leaders and employees of non-profit organizations) were used.It is already possible to talk about the positive impact of the Network on the dialogue between different sectors of society. Each of the parties has its advantages from the emergence and spread of the Internet: society get accessibility of services (state, local, non-profit organizations); power get simplification of procedures for regulating and controlling civil society (the ability to monitor sentiments in different territories, groups). Public receive the availability of government support measures for NGOs and civic initiatives, as well as the popularization of the non-profit organizations themselves. In the Yaroslavl region, the Internet becomes an important communication channel in the intersectoral interaction, which forms a new culture of communication, and a mechanism for consolidation. The study confirmed the hypothesis that the Internet development facilitates communication with civil society actors (obtaining information and access to power).At the same time, it was revealed that the development of the Internet network makes it easier to control the subjects of civil society and makes the manipulation technologies more productive.

The study examines the main vectors of the impact of social networks on the form of political struggle, which, although partly acquiring a virtual nature, does not change its content. Based on the analysis of quantitative data of “Yellow vests” protest sites, as well as surveys on the social structure of the participants, the study deals with the technology origin and functioning of a new phenomenon of political linguistic and communicative community, acting in the social networks and is not connected with the traditional “really organized” political institutions and groups. The study focuses on the “political power” of this virtual community, i.e. limits of its influence on political decision-making at the national level.

Online segregation is among the most commonly discussed phenomenon. This paper calls into question the need to focus on the dangers of echo chambers, filter bubbles; furthermore, it proposes to identify latent factors of the Internet segmentation and specify communication in homophilic communities. Thus, the current study aims to detect the mutual influence of social network choice and political behavior among Russian students. The study is based on empirical data obtained by a survey conducted in 2018 in St. Petersburg. Our research has revealed that students are a heterogeneous group. The identified four factors described as “Web-services for full-grown people,” “Mobile services,” “Closed silo of content,” “Audiovisual services” disclose hidden relationships between quite different online services and political identification of students.

The paper provides a discussion on the understanding of information ethics as a research field which scrutinizes ethical problems of social communication in close connection with the analysis of cyber informatization process. It is proposed to consider information ethics as a generic concept and interpret computer ethics as one of its types due to computer communication technologies. Historical types of information ethos and its psychological characteristics are revealed in accordance with the main types of information paradigms. The basis of this paradigm is a specific type of system ‘human – information technology’ and information exchange technology. In the structure of the information exchange several components can be distinguished: (a) content component, or the actual exchange of the information (content); (b) technological basis of communication which generates the ethics of technological methods of information exchange; (c) psychological aspects of information exchange which change together with each new technological method of communication. Its changes raise the specific ethical problems and form a specific type of information morality – information ethos. Ethics of information exchange and ethics of information technologies are distinguished.

The article explores the digitalization as the process of increasing use of digital technology in global politics. It particularly focuses on digital diplomacy as an instrument of agenda setting and presence expansion in social networks. Author investigates how state bodies and political leaders communicate on Twitter with regard to 2019 Venezuelan presidential crisis and who promotes the position more successfully. The dataset (n = 9,707,730 tweets) covers the period from December 1, 2018 to March 10, 2019. The results display the existence of phenomenon of “digital diplomacy of (non)-recognition”, the importance of tweeting in native language of target audience, underestimation of the use of hashtags, and, finally, the information domination of supporters of Venezuelan opposition on Twitter. The conclusion of the research is that political communication on Twitter has become an important part of digital diplomacy in crisis situations as it delivers messages quickly and efficiently both to official departments of different countries and to wide international audience.

The paper describes a scientific study on the adaptation of smart city technologies in Saint Petersburg. There have been several attempts to implement the smart city concept but the adaptive capacity and the demand for new technologies by citizens remain underexplored. The research was conducted using a socio-technical approach according to which the impact of technology on society and the formation of technology by society are parallel processes. As a place for research approbation the pilot region St. Petersburg was selected. By examining a survey of city residents, this paper sheds light on an array of perceptions citizens have regarding smart cities, readiness to use new technologies and trust in the opportunity to participate in city management. The research results suggest a high level of information technologies usage and existence of the positive experience in obtaining online services. At the same time almost 1/3 of respondents didn’t want to have any experience in electronic interactions with the authorities or even personal communications. The level of smart city awareness reached 11% among those respondents who clearly understood what it is and have any ideas of its implementation in Saint Petersburg. Almost 46% of the interviewed citizens could not demonstrate any knowledge on smart city concept or projects. The most frequent users of e-participation portals among the respondents from St. Petersburg are citizens aged 26–35 years. At the same time, their evaluations of such experience as positive or negative are divided almost equally. The obtained results could be used to develop recommendations for the optimal implementation of the “smart city” concept (in cooperation with the project office “Smart City of St. Petersburg” under the Administration of St. Petersburg).

The systems-technical analysis of the processes of collecting, storing, processing and analysing of Big Data arising in the tasks of environmental monitoring in the framework of the «Smart City» project is considered. A conceptual Big Data processing model of an environmental monitoring based on a NIST Big Data Reference Architecture is proposed.

Analysis of emotions has received recognition in urban studies as a mean to understand subjective quality of life. Availability of spontaneous user-generated online urban data generated by users in location based social networks broadens possibilities for such analysis as described in a number of studies. However the LBSN data is not shared deliberately by users and is not meant to be an expression of emotions, which makes its representativeness and validity questionable. Another source of data - public participation geo-information systems - helps to overcome these limitations however may have its own, such as a small and biased sample. In this paper the results of the comparative analysis of the distribution of emotions in St. Petersburg, Russia, visualized with LBSN and PPGIS data, are presented. The dataset is formed from user-generated comments on urban venues from Google Places and data from PPGIS platform Imprecity ( www.imprecity.ru ), where citizens deliberately share their emotions and comments about public spaces. The data samples contain 1800 emotional marks from Imprecity and 2450 geolocated comments from Google Places marked by experts and then processed with Naïve Bayes Classifier. Comparison of positive and negative emotional maps created for Imprecity and Google Places shows shared tendencies in emotional distribution, such as concentration of emotions in the city centre and collocation of positive and negative emotions. There are also differences in emotional distribution: PPGIS data shows local “emotional” islands, which correspond to pedestrian areas and green spaces. The comparative analysis appears to be insightful and capable of revealing recurring spatial tendencies in subjective perception of the city.

The paper discusses about interoperability mechanisms in the scale of urban IT infrastructure. It represented by a variety of interacting information systems and requirements for complex structural layers that construct urban networks. Government information systems, according to the existing legislation, automate only a certain list of priority functional services, which leads to insufficient flexibility of data exchange framework. They create a unified information space with key tools of interdepartmental interaction processes. Historical incompatibility and heterogeneity of the urban infrastructure elements prevent smart city technologies introduction, that also generating typical problems for interacting systems with each other. The long-term solution to this problem can be provided by changing requirements for technologies and interaction rules: restructuring existing government information systems, reorganizing the interaction of enabled systems, changing the architecture principles of urban infrastructure. And it’s also include the advanced multi-stage study about different infrastructure levels. Based on these systems interoperability research, recommendations for the development of urban information framework are given.

Traffic congestions have a strong impact on the life of modern cities. Reducing congestion is one of the main concerns of private, urban and public institutions. Much effort has been devoted to the scientific research of this problem. One of the areas of these studies is the prediction of congestion. The forecast helps to distribute traffic on urban highways, thereby minimizing the congestion of individual sections and improve the road situation as a whole. There are special Internet services that analyze traffic congestion and provide users with information. They make a forecast of traffic congestions on the basis of statistics, however, as practice shows, these forecasts are not very accurate. Recently, neural network forecasting methods have been actively developing. In this paper, we investigate the possibility of recurrent neural networks with controlled synapses to predict traffic congestions. On the example of the famous Internet service “Yandex.Probki” is shown that the neural network is able to give more accurate predictions.

According to previous research, a high degree of Openness and Neuroticism, and a low degree of Agreeableness are personality determinants of e-shopping. This study aims to explore the relationship between the Five-factor model of personality (i.e. Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) and e-shopping in a Swedish context. In a nationally representative sample, a questionnaire was distributed to 3400 citizens. The response rate was 53 percentage (N = 1812). The questionnaire included measures of the Five-factor model of personality (BFI-ten) and e-shopping. Multiple regression analyses were conducted to test if the Five-factor model of personality predicted e-shopping. The dependent variable was self-reported frequencies of e-shopping during the last 12 months. The first analysis showed that Openness is predicting e-shopping. However, this effect disappeared, when age, educational attainment and income were controlled for. Our conclusion is that the Five-factor model of personality is a poor predictor of e-shopping and that e-shopping frequencies are unrelated to the personality of internet users. Methodological limitations are discussed, for instance the use of a single-item for measuring e-shopping and a short-scale for measuring personality. There are difficulties comparing our findings with previous findings, since the concepts personality and e-shopping have not been defined uniformly. The analyses revealed significant variation in definitions, measurements and methodologies. Caution should also be taken in generalizing the present results to other countries and other time periods.

The e-commerce market has been developing rapidly in recent years. The number of consumers making online purchases is increasing. In conditions of increasing competition in the market, a large selection of products and brands, increasing consumer power and ability to switch to competitor’s products through the internet, research on consumer loyalty factors in e-commerce market becomes relevant. This paper is devoted to the study of consumer loyalty factors in the Russian e-commerce market. The findings are based on the results of an empirical research implemented in the form of an online survey of 601 consumers in the Russian e-commerce market. The main attributes and factors of consumer loyalty are highlighted in the study. As a result of factor analysis, 9 factors are identified. Based on the results of factor analysis three clusters of customers are defined. The paper contributes to studies on customer loyalty factors in Russian e-commerce market and may be used as a base for future research.

The paper provides an overview of existing digital competence frameworks and identifies their commonalities, differences and potential complementarities to help researchers, practitioners and policy makers develop better managerial interventions. The paper offers an integrative framework that specifies consumers’ digital competences, their antecedents and consequences. A proposed approach emphasizes the need to investigate consumers’ motivations, opportunities and knowledge as equally important prerequisites of effective and efficient consumer behavior in digital consumption encounters. Besides, an approach goes beyond the registration of consumers’ digital behaviors, but requires estimating their efficiency. Directions for development of optimal intervention strategies to stimulate consumers’ digital behaviors are proposed. The paper concludes with a set of propositions for further development of the consumers’ digital competence framework.

In this paper we reflect on the potential employment impacts of national digital strategies. Traditional industries will be affected, and many employees will lose jobs. Ensuring their adaptation to digital economy is a responsibility co-shared between national governments, universities and employees themselves. We compare government investments into higher education and lifelong learning programs between OECD countries and state that there is a need for systematic continuous educational programs on digital skills where they are underinvested.

Companies search for new ways of utilizing technologies such as the Mixed Reality (MR) in order to enrich their customer interactions. While more of these MR technologies are emerging to assist customer-employee interactions, there is a strategic choice related to scalability of how to organize these service encounters: face-to-face or digitally over the web. Eventually, the question is how much of these interactions can be automatized with acceptable tradeoffs for the customer experience and business outcomes. This study analyzes the influence of a MR design elements on the outcomes of a customer experience in a use case where the customer learning is focal for the service. The experiment comparing two conditions: face-to-face interactions and remote interactions over the web showed no difference in terms of customer experience and perceived learning. On the other hand, the ease-of-use of the technology as well as the familiarity with the subject and technology effected the customer learning. The results offer implications to both the customer experience management and the MR system design.

The paper proposes the empiric research of socio-cultural factors’ impact on the national technology development. It is well noted that the rapid technology development gives an increase of living standards, boost country economy, and contributes substantially to the gross domestic product. Being intangible socio-cultural factors are still potential drivers to the technology development enhancement and play an important role in constituting national welfare.The aim of the present research is to empirically investigate whether socio-cultural factors have significant impact on the national technology development and whether Hofstede’s indices play a mediating role between chosen factors and the technology development. The literature review has shown that results of the similar empiric research are rather controversial. Based on a dataset from more than 100 countries we have designed and analyzed a second-order model that confirmed the impact of the chosen factors to the technology development at a national economy. The most unexpected result deals with the direct impact of the business culture (R2 = 0.92). The received results also confirmed the role of the human capital and the social capital. Hofstede’s indices play a mediating role in two from three cases: between human capital and the national technology development and between business culture and the technology development.

The article analyzes the possibilities of maintaining the register of intellectual property objects using blockchain technologies. Such registries have a number of advantages in comparison with traditional methods of registration and provide more opportunities for rightholders. However, they also have certain disadvantages, primarily due to the lack of full-fledged mechanisms for resolving intellectual property disputes. This problem requires not only technical, but also organizational and legal solutions. The authors conclude that the prospects for the development of blockchain platforms for registering and managing intellectual property are linked to the possibility of dividing such registries into two types. The first one is the registries of IP objects on the permissionless blockchain platform. Information about IP objects and their authors (owners) contained in them will not be official. The second type is the registries of IP objects on the basis of permission blockchain technologies, in which superuser rights will be granted to government bodies authorized in the field of IP protection. Entries in them will be official and will have validating rights value.

The rapid development of the possibilities of obtaining ubiquitous use of IT brings the digitalization of all processes, services and products to society at large. This development provides the opportunity of creating novel business models, reaching new market segments with novel products at cheaper/better and more targeted price levels. In this paper, we demonstrate the wide range of possibilities that are emerging with digitalization of a business environment in the e-health field, deriving from a process of digitalized business modelling. We discuss this approach by presenting a case of e-health in the form of an IoT solution for creating a safer home environment, and how it can be offered in different ways to its end users. The use of creative business modelling can thus be demonstrated to make emerging internet technologies available to broader segments of society. The ways that value networks interact and distribute incomes and costs create new methods of providing new services to new people, thus driving the digital transformation of society forward. The paper concludes with a set of principles for approaching creative business planning in a digitalized business reality with an e-health focus.

The digitizing economies call for new methods in studying social-economic phenomena that are often short-lived and for which no pre-identified set of indicators had been developed. In our paper we demonstrate how intelligent analysis of online data can supplement the use of more traditional methods, such as the ones relying on official statistical reports or sample surveys. We outline benefits and disadvantages for each group of methods, and also identify some challenges in joining the data obtained from the diverse sources – particularly, the classification of the data per industry sectors. The data that we used for building ARIMA models were obtained with our dedicated labor market monitoring software system, operating from 2011 and currently containing 10+ million unique data records for vacancies and resumes. We found that for average wages the official statistics data can be approximated (error 7.82%) and possibly refined by the wage levels that companies offer in the openly posted vacancies ads. Further, we constructed predictive models for the employees’ demand by the companies and found positive and negative influences (Lag −2, Lag −3 and Lag −4) for several industry sectors for which online data had been collected. The data from the identified groups can be used as leading indicators to predict situation on the labor markets.

Popularity of social networks makes them an attractive field for analysis of users’ behavior, for example, based on the intention analysis of their posts and comments. In the linguistic theory only 25 types of intentions exist and can be joined in 5 supergroups. We use the dataset that contains directed oriented graphs which nodes store information about the author intention, text of the post in the social network “Vkontakte” etc. Each graph is split in a linked list of nodes (a sequence, 13156 sequences in our dataset) from root to each leaf so that the intention prediction becomes the sequence prediction. We have analyzed traditional and neuronet approaches that address this task and proposed to solve it with the original modifications of CNN and RNN architectures. It was decided to translate all posts to the embeddings which are then used as inputs for our neural network. According to the benchmarking experiments, we have identified that the proposed RNN architecture outperforms other alternatives. Also, predicting supergroups is done more accurately. Finally, we found out, that the context in the dialogs is lost quickly that allows to decrease the algorithm context size while keeping accuracy at the appropriate level.

Messenger Telegram launched in 2013 but became popular among users in 2015. This was due to several statements that were taken for granted as absolute truth. One of such allegations was the influence of the Telegram on the information field of the country. The article discusses the dynamics of the discussion on the acute social issue - the status of the study of the national language in Tatarstan in the messenger and in the mass media. The detailed analysis of the development of an informational event in time has been carried out. We analyzed the positions of the actors included in it and their role in the dissemination of information.

The Internet has become an extremely important means of human communication. However, it is also used by extremists and terrorists who employ it to promote their ideas and recruit new members. One of the tasks facing academia and governments is detection of extremism/terrorism related content and counteracting a corresponding ideology. Analysis of linguistic features of texts from extremist sources can help to solve this task. The aim of the research is a corpus-based keyword analysis of Russian-language posts from extremist forum and texts by common Internet users on the same topic (comments on the 2010 Moscow Metro Bombings). We also used a corpus of blogs from LiveJournal as a reference corpus for deriving keywords, which is standard practice. We performed keyword analysis by means of WordSmith Tools software package and used qualitative, manual discourse analysis to identify a number of differences between texts from extremist forum and texts by common people on the same topic on lexical level.

This article presents a first phase’s results of research project, dedicated to developing prognostic models of User’s Image with the automated Social Networks data processing methods. A pilot study carried out a comparative analysis of automatic methods for analyzing textual and visual data, which constituting a User’s informational image. We used a correlation analysis of data, which was obtained by using parsing methods and the results of psycho diagnostic research. As a result, multiple relationships with the socio-psychological characteristics of the respondents were identified. Based on obtained data, we have concluded about acceptable predictive capabilities of automated analysis.

This paper investigates on how spectators communication is organized in chats during broadcasts on Twitch.tv with the main focus on toxic communication. The main purpose of the paper is to understand how socio-demographic characteristics of a broadcaster and channel settings which broadcaster can control affect communication in a chat. Chat logs from Twitch.tv channels were used to create a topic model of viewers discussions. The result of regression analysis indicates that socio-demographic characteristics of a broadcaster have a statistically significant effect on the type of communication, which is manifested in chat.

Numerous cultural events take place around the world every year. Visitors leave digital footprint after attending such events, which is a good source of data analysis in tourist behavior and cultural studies. This research provides mapping of festival themes associated with the annual cultural event “Museum Night” on social networking site (SNS) VKontakte (VK) most popular in Russia. All posts containing the official event hashtag in Russian (#нoчьмyзeeв) were collected from VK. To analyse the data, more than 38 k posts spanning 2012 to 2019 are used. The results show the dynamic of the event web activity and changes over the last years.

ITMO university has 4 megafaculties, 14 faculties, 5 institutes. It’s really hard to find the great scientific supervisor in your professional field. We wanted to make the simple tool to find the right one. At this study we have collected research information associated with ITMO university from different sources, including papers from PubMed and ArXiV. We also used publicly-available information provided at ITMO University’s website (which is the first thing every prospective young student might find, even if he doesn’t belong to ITMO University yet). At this work we focus on exploratory analysis of the data we collected.

This paper operationalizes ingroup and outgroup image construction in the Russian lesbian-feminist movement discourse. To investigate the speech properties which are involved in the radical social movement discourse dissemination we employed semantic network analysis. In the study were analyzed two sources of data: from 574 lesbian-feminist groups in the social network “VKontakte” and from the 18 interviews with self-identified members of Russian lesbian-feminist community. Differences in the image construction in examined environments were discovered. The methodology for the “we” and “they” representations investigation presented in this article could be applied to the study of the other radical social movement’s discourses propagation.

GitHub is a large platform that allows developers to host repositories with code and collaborate on various projects. With the development and expansion of open-source software (OSS) many researchers focused on various aspects of such open-source communities. Due to the availability of a wide range of projects, newcomers have an opportunity to be involved in ones that differ in terms of skills and experience required. However, new developers often face some barriers during the onboarding process. The aim of the current paper is to investigate relations towards newcomers through sentiment analysis of comments they receive in issues and pull requests in repositories of top-10 open source projects by contributor count and top-10 fastest growing open source projects based on The State of the Octoverse 2018 report by GitHub. By applying sentiment analysis we focus on differences between reactions to contributions of ‘old’ and ‘new’ developers, and find that while the majority of comments is rated as neutral, the amount of negativity is slightly higher for newcomers.

Information from users’ profiles on social networking sites is an important data source for analysis of the users’ psychological characteristics. Texts, video and audio files, images, public pages can be easily accessible and analyzed. We consider the ways of estimating the users’ psychological characteristics on the base of his or her profile in the social network VKontakte. We compare different machine learning models for the analysis of user’s texts, such as linear regression, decision trees, random forest, support vector machine with linear, radial and sigmoidal kernels. Also we discussed the possible further stages of research including the sentiment analysis for better text description, the analysis of profile photo, and, finally, ways of combining all steps for estimating psychological characteristics of social networks users.

The article is devoted to the research of the modern machine translation systems as well as different aspects of their using while development and in the course of functioning of adaptive computer system for individual testing of students’ knowledge. The contrastive analysis of computer-aided translation and automated translation is reported, computer-based translation in its three main modern sorts: based on rules, statistical and hybrid is estimated in the context of technological opportunities of application by both linguistics specialists and in the field of component development of learning management systems.In the article the approaches to the problem of adaptation of computer system for individual testing of students in preparation of bachelors of engineering degrees are stated. The peculiarities of the use of machine translation systems for training course development, structuring of test material and in the process of realizing of adaptive algorithm for individual testing of students’ educational progress are discussed.

The field of education is relatively conservative, but at the same time fast evolving sphere of human activity all around the world and in Kazakhstan as well. This is due to constant advances in all technology areas and in information technology in particular. New teaching and learning approaches, methods and techniques are introduced into the educational process, applying various technologies like artificial intelligence, computer vision, robotics, etc. And virtual reality is one of those. Virtual laboratories are one of the computer-based learning systems that can help to study various processes (physical, chemical, etc.) simulating and visualizing them on a personal computer without using the actual equipment or reagents. Moreover, with the help of such simulations it is possible to observe the process in detail, from different points or enlarge the image to a convenient size. In this paper an application that helps to study physics in secondary schools is presented. It contains a set of practical problem tasks from a number of physics sections. Each task has visualization scene with virtual reality integrated. In the article the content, architecture and interface of the application are presented along with the short review of other research in the field of application of virtual reality in education.

The purpose of this article is to present the results of the ongoing study aimed at development and implementation of the specific mobile application – “aviation glossary” within the educational CLIL model on the basis of Saint-Petersburg State University of Civil Aviation, Saint Petersburg. The authors examined the phenomenon of content and language integrated learning, its features and components. At the same time the authors appealed to the newly-developed branch of e-learning – mobile learning, its models based on the “Bring your own device” concept and its key advantages for everyone involved in educational process. The presented article is also devoted to the description of lexicographical work of the m-glossary composition and its further usage with the “autonomous learning” model to develop students’ linguistic skills, e.g. vocabulary knowledge, reading comprehension of the professionally-oriented text. The findings revealed a stable increase in the rate of students’ vocabulary knowledge which is required in order to perform professional and communicational activities successfully.

In this study, a simulated, VR-based environment was built and analyzed to explore if a VR environment can possess recovering effects. 61 university students tested a VR application depicting a forest and answered survey questions about the experience. The results showed that VR-environment can indeed have recovering effects. Moreover, when comparing to previous studies in real forests, the recovery effects were at similar levels. The study results suggest that as the VR-based environments can possess recovery effects, they can work as recovery environments at schools or similar environments. The study results offer implications for the designers and propose design principles to build recovering VR environments. Future research avenues to scrutinize the results in various research contexts are discussed.

Due to the global integration processes in education (academic mobility, the increasing number of students, etc.), it has become difficult to provide a proper individual approach to each student during a class at university. Learning management systems (LMS) are viewed methodically as a means of providing this individual approach to satisfy the demands of a massive student audience. The purpose of the paper is to select the most effective interactive exercises available on Moodle platform for developing the linguistic competence and organize them in a proper sequence that would provide coherent linguistic competence development. Key notions are described in the theoretical part of the paper; the criteria determining raw data and further data analysis are provided. Quantitative methods of data analysis have been employed. The practical value of the research consists in providing the sets of interactive exercises based on objective criteria. The possible sets are presented in Appendix. A code in Python was elaborated to perform data analysis and can be used to deal with similar research problems. Researchers who are interested in the code are welcome to email the third author of the paper.

Academic writing is a skill that increasingly attracts attention in modern university. By teaching writing skills to students, especially students in humanities, we can offset the drawbacks of today’s information overloaded environment, develop students’ critical thinking, clarity of thought and ability to creatively engage with textual material. This article is based on the experience of developing and teaching a master’s level course that specifically focuses on note-taking as a subset of academic writing and combines digital and analogue tools in an attempt to teach students more efficient note-taking and information management skills. The data gathered through quantitative and qualitative methods help to elucidate students’ note-taking and writing practices and to highlight the areas in need of further attention.

The present study explores the methods of geodata visualization extracted from the metadata of scientific publications for use in scientific research using the scientific heritage of Georgy Gause. It is based on the results of case studies to assess the possibilities of digital information resources, metadata extraction from digital resources, and using methods for their quantitative processing. We have studied methods of extracting metadata from digital information systems that do not have export tools. Our concentration is on methods and technologies of geodata extraction, and their subsequent visualization are considered. They are considered and applied methods of a dynamic clustering of geodata markers. Based on geodata visualization, we interpreter the results. The possibility of using extracted metadata in scientific visualization systems that support standard formats is evaluated.

The article explores the key trends in news consumption among Russian-speaking migrants in Israel in years 2006–2018, representing the first longitudinal evaluation of news consumption patterns of this social group. The analysis is based on the open data collected from 2006 to 2018 by news agency newsru.co.il, ranked by SimilarWeb as 28th of all Israeli websites. Large-scale surveys, aiming to reveal media consumption patterns among visitors of the news agency website, consisted on 25–30 closed multiple choice questions and involved circa 2000 respondents. The study highlights how the choice of media for news consumption has changed over twelve years, reveals the heavy digital character of news usage and discusses the habitual information check in high choice news environment.

The final state attestation in the demonstration examination format becomes obligatory in 2020 for the educational organizations providing students with the working professions included in the list of the most demanded and perspective on the labor market. In this regard, it is necessary to develop the most effective practices to complete the demonstration exam tasks. This article discusses the approach of using subtasks and their relation to the corresponding actions in organizing students’ training for the demonstration examination “Web design and development” and its successful passing.The approach described can also be applied to training for WorldSkills standards on different levels both for “Web design and development” and other skills. The conclusions and solutions formulated in the study are the basis for the creation of the information system to support students’ training for the demonstration exam.

This paper is devoted to the problem of gender inequalities in the Soviet children’s literature. There gender stereotypes are considered along with the influence of the authors’ characteristics on the representation of these stereotypes in Soviet children’s books. The main attention is paid to the author’s generation, his/her gender and the socio-economic status of the author’s family. The measurement of the socio-economic status of the author’s family is provided with the coding of the author’s parents’ profession using the ISCO and the ISEI. This research paper highlights the main types of characters that are identified through the cluster analysis. It also discusses the characteristics of literary characters that can influence the level of the gender stereotypes representation.

New technologic environments secure to their users sophisticated exit routes to intrusive publicity. There is, actually a crescent recognition about the sophistication of consumers and the complex relationship between propaganda on the digital world. Such a position is supported by 200 million users of Ad blocking software estimated throughout the world. It seems clear that relevant, smart and attractive productions are key pieces to perpetrate advertising as a business in this environment. However, how does the social networks user synthesize these concepts? What are the motives that lead users of digital media to expose themselves to advertising messages? In view of the fact that the nature of the advertisements consumption requires a high degree of interest from the audience member for their exposure, we have conducted a study in Brazil and Portugal, developed by applying the method of Grounded Theory, firmly rooted in one hundred and eighteen in-depth interviews conducted in 2016 and 2017. The text is dedicated to introduce the three most consistent motivations categories towards exposure to advertisement, A. Information/Surveillance B. Humour Management and C. Personal Integration.

In this study authors examined a children experience of using digital devices. Previous research has shown that rapid technological changes increased access to mobile devices for preschoolers and primary school children. The new measuring tool – Icon Recognition Test – was developed by authors within the scope of the study. The psychometric properties of the test were discussed. It was shown that the experience of mobile devices usage enlarges in the context of age shifts in leading child activity from playing to active interactions with social and physical environment. The findings revealed that children have predominantly known those mobile apps which are widely used by adults.

The present article considers the impact of information technologies on the development of pre-school children, which, we believe, is relevant owing to the rise of interest showed by young children in digital technologies. Computer and mobile games firmly hold the lead among the top entertainments for children. The present study presents the theoretical and experimental basis of the idea that computer games contribute to the development of practical thinking. Drawing on the classical ideas expressed by L. S. Vygotskiy who believed that human mental processes can be changed the same way as practical activities, we analyze some modern approaches and policies aimed at the development of higher mental functions.The article discusses the results of the educational experiment, which explored the impact of educational computer games on the development of the way of thinking in children as young as 6–7 years. The investigation consisted of three stages: the first one introduced the diagnosis of the current development, particularly, such types of thinking as conceptual speech thinking, abstract thinking, and practical thinking and constructive thinking. The second stage included the educational experiment with realization of specific programs. The selected programs are focused on the age group between 5 and 7 years, they are designed taking into account the peculiarities of the children’s attention. The third stage performed a repeated control diagnosis. Comparing the data of two diagnosis allowed us tracing the changes, which happened after introduction of educational computer games.

Participants of social networks experience a temptation to build multiple profiles/identities which are homomorphous (sometimes isomorphic, often contradictive) to their real-life identities. While this experience may be viewed as a masquerade, it’s hard to deny psychological grounds of possessing multiple online identities. Every time a social networker owns two or more profiles, they are referred to as alternative identities, irrespective of which is ‘real’. Participants: 42 social networkers 15 to 25 years old, half of them females. Each was presented an Identity Dilemma, which involves issues of online identity and moral development. The dilemma was worked out as a part of the Good Play Project (Harvard Graduate School of Education), used by permission from the developers. Semi-structured interviewing procedure included putting selected questions to the participants while discussing the dilemma issues. By classification of interview narratives the following attitudes were selected, referring to alternative identities: affective, cognitive, and behavioral. Dispersion analysis and content analysis were performed to handle the data. Differences in attitudes, dependent on age, gender and identity parameters are described.

The article presents the results of a study aimed at the analysis of the relations between cyber-aggression and problematic behavior in adolescence. According to K.C. Runions’s theory of cyber-aggression four types of online aggressive behavior were studied: impulsive-appetitive, impulsive-aversive, controlled-appetitive, controlled-aversive [1]. Data collection was carried out with Cyber-Aggression Typology Questionnaire, Strengths and Difficulties Questionnaire, Coping strategy indicator, Questionnaire for the assessment of the adolescents’ involvement in Internet communication. The study involved 130 adolescents aged 10–16 years, 56.2% female. The results suggest that the appetitive cyber-aggression is more common in adolescence than aversive forms in spite of increasing manifestations of controlled-aversive cyber-aggression in older adolescence. Problematic behavior is a predictor of appetitive cyber-aggression only for older adolescents (15–16 years old), but these relations are not found for younger adolescents (10–12 years old). This fact is discussed according to the idea about cyber-aggression as a form of social experimentation in younger adolescence.

The paper presents an implicit model of the positive and negative effects of computer games as perceived by their young adult users reconstructed from the results of psychosemantic analysis. The participants in the empirical study were 117 higher-education students in Saint Petersburg, Russia, aged between 18 and 30. To detect patterns of subjective meanings describing the effects of computer games, a repertoire array with fixed constructs was devised. Its elements were the 10 computer games most popular among young people. The constructs were formed from the results of a content analysis of the respondents’ unprompted descriptions of the single-player games selected with an indication of their positive and negative effects on a person. The basic semantic constructs that shape the students’ concepts of the effects of video games are “Recreation” versus “Training thinking skills” and “Immersion in the virtual reality of the game” versus “Awareness and volitional regulation of activity”. Differences in concepts of the positive effects of gaming were found to depend on the amount of time respondents spent playing. A gender distinction in implicit concepts of the effects of video games was identified.

Procrastination is a prevalent and problematic, yet seldomly studied behavior. Twitter provides perfect examples for proud and productive procrastination through the deliberate self-representation of procrastination. Using manual coding and machine learning aided coding content analysis, this study uses 4587 tweets contained the hashtag #procrastination, and the author aims to identify the genres of tweets disclosing this behavior and further discuss the notion of value creation as sense-making [1] with this particular hashtag. Results suggested that structured procrastination was more likely associated with positive impact and it was more likely put in an ironic tone on Twitter. This finding is in line with the self-enhancement strategy when people represent themselves on social media. Data also show that people were more likely to self-reflect at critical time nodal points in a week, such as the end of weekdays and weekends. This paper offers theoretical and practical contributions to understanding the socio-cultural and psychological function of social media in the case of disclosing an arguably negative, yet prevalent behavior, procrastination.

The paper is focused on the significance of Assistive Innovative Technologies to Neuropsychological Assessment and Neurorehabilitation. The purpose of this study was to investigate the usefulness of Technology in clinical assessment and rehabilitation practice for children with Developmental Dyslexia (DD). Participants and Methods: 45 third-, and fourth-grade students with DD and a matched control group (n = 45) participated in this study. At the beginning, students underwent a clinical assessment, including both Electrophysiological [i.e. Event Related Potentials (ERPs) esp. P300 wave-form] and Neuropsychological tests, being conducted in Laboratory of Neuropsychology, at University of Thessaly, in Volos, Greece. Children with DD scored a statistically significant lower performance, compared to that of the typical readers. After assessment, a subgroup of children with dyslexia received Rehabilitation Services. The Rehabilitation Program was specifically designed for children and included digitized musical activities, being converted to sound files, via music training auditory Software programs. Results: The electrophysiological results, obtained after the rehabilitation program revealed that children had similar P300 latency values to that of the typical readers, thus children addressed their difficulties and became successful readers. Conclusions: The outcomes of the current study suggest not only that Technology plays a vital role in both Assessment and Rehabilitation approaches but also, as it continues to move forward, more and more assessment and intervention environments could be designed, providing significant benefits to individuals, thus making humans realize the new opportunities of Science and Technology.

The paper presents the main aspects of implementation of the Russian-Tatar Sociopolitical Thesaurus. This resource is inspired by the Russian RuThes project and is built as a hierarchical model of the sociopolitical terminology of Tatar. The thesaurus reflects the logical-semantic organization of lexical items (synonymic, generic, and other relations) on conceptual and lexical levels. Currently it comprises vocabulary related to state government, economy, social life, justice, warfare, culture, religion, sport and some other basic topics.We developed a set of grammatical models of corresponding Russian-Tatar paired units and analyzed them in terms of semantics and structure. In this paper the main attention is paid to one-component corresponding items. The results of this work are enabling us to improve the quality of bilingual linguistic applications.

Traditionally, it is believed in linguistics that the center of any semantic field is more stable than the periphery. Quantitative testing of this hypothesis has become possible due to creation of large diachronic text corpora. The article describes the results of quantitative analysis of “central elements” (semantic dominants) of 82 synonymic sets, which were taken from a dictionary by Yu.D. Apresyan. First, we identified the most frequent words in each synonymic set. Then, we analysed the dynamics of their frequency over the two centuries, according to the Google Books Ngram corpus. It was found that the semantic dominants show a statistically significant tendency to decrease in frequency with respect to other members of the synonymic sets. It was also shown that frequency of the synonymic dominants is more stable in comparison with randomly selected words which have a close frequency.

Study of cause-effect discourse connectives can help in automated discourse processing and automatic identification of argument units. Besides conjunctions and other functional words and expressions, there are different multi-word expressions containing content-words (e.g. is etogo sleduet ‘it follows that’) that have the function of cause-effect connectives. We present a method for connectives mining for Russian language. Firstly, the seed list of 143 multi-word connectives was manually extracted from the Ru-RSTreebank corpus. Two Word2Vec models, trained on the news corpus, were used to detect new multi-word connectives. Before first model training, connectives from the seed list were glued to build multi-word tokens. Before second model training, in addition to it, all 3-grams in corpus, that correspond to the specific proposed patterns based on anaphoric expressions, were also glued in the same way. The method based on the second model gives a satisfactory result and lets expand connectives list for cause-effect discourse relations, after manual editing (286 new connectives).

Digital technologies provide new possibilities for studying cultural heritage. Thus, literature research involving large text corpora allows to set and solve theoretical problems which previously had no prospects for their decision. For example, it has become possible to model the literary system for some definite literary period (i.e., for the Silver Age of Russian literature) and to classify prose writers according to their stylistic features. And more than that, it allows to solve more general theoretical problems. The given research was conducted on Russian literary texts of the early 20th century. The sample included 100 short stories by 100 different writers. The measurements were carried out for 5 syntactic variables. For each of these distributions, the most popular statistics were calculated. Basing on these data, we consider empirical verification of Lyapunov’s central limit theorem (CLT). The article validates the effectiveness of CLT theorem and the conditions for its implementation. Besides the normal (Gaussian) function we used another analytical model—the Hausstein function. It turned out that both theoretical distributions for each of five variables do not contradict the experimental data. However, the alternative analytical model (Hausstein function) has shown even better agreement with the experimental data. The obtained results may be used in computational linguistic studies and for research of Russian literary heritage.

The poetic texts pose a challenge to full morphological tagging and lemmatization since the authors seek to extend the vocabulary, employ morphologically and semantically deficient forms, go beyond standard syntactic templates, use non-projective constructions and non-standard word order, among other techniques of the creative language game. In this paper we evaluate a number of probabilistic taggers based on decision trees, CRF and neural network algorithms as well as a state-of-the-art dictionary-based tagger. The taggers were trained on prosaic texts and tested on three poetic samples of different complexity. Firstly, we suggest a method to compile the gold standard datasets for the Russian poetry. Secondly, we focus on the taggers’ performance in the identification of the part of speech tags and lemmas. We reveal what kind of POS classes, paradigm classes and syntactic patterns mostly affect the quality of processing.

Natural language processing (NLP) is a rapidly developing field of research. In solving the problems of NLP, along with traditional methods based on a statistical model of language, machine learning methods (ML) are used. The paper considers bibliometric indicators NLP and ML. Dynamic indicators are evaluated and areas of research with the highest growth rates are identified. The indicators were calculated for the following NLP applications: Grammar Checking, Information Extraction, Text Categorization, Dialog Systems, Speech Recognition, Machine Translation, Information Retrieval, Question Answering, Opinion Mining, Smart advisors, etc. The greatest values of dynamic indicators are demonstrated by: Grammar Checking, Information Extraction, Machine Translation, Question Answering. At the same time, the following methods of solving NLP problems are developing most dynamically: Machine Learning, Deep Learning, Neural Networks and Supervised Learning. In turn, Deep Learning is used to solve a wide range of applications. Its feature is the increased requirements for the volume of data processed. The paper assesses the growth dynamics of bibliometric indicators for some Deep Learning applications. The most dynamically developing research is the field of deep learning applications to solve healthcare problems.

This paper presents an approach to Russian text vectorization based on SRSTI classifier. Our approach is based on using SRSTI categories as vector space dimensions. The categories are defined by lists of keywords. We explain our choice of SRSTI as a basis for vector space. We describe the keywords selection process, as well as vector calculation and comparison algorithm. We apply developed algorithm to marked-up SRSTI texts and user social profiles. We also suggest approaches to vector space improvement and evaluate them.

We propose a measure to quantify strength of semantic relation between two pairs of binary alternatives in cognition of a subject group. The measure and experimental methodology, adapted from physical toolbox for detection of quantum entanglement, allow for on-line realization when a WWW search engine functions as a model of collective cognition of its users. We demonstrate the method by quantifying culture-specific semantic relations between the archetypal living creatures and their qualities, which previously could be addressed only verbally. Methodology and state-of-the art in quantum approach to semantic modeling of human behavior are outlined.