The Palgrave Handbook of Digital Russia Studies
Deep learning is a term used to describe artificial intelligence (AI) technologies. AI deals with how computers can be used to solve complex problems in the same way that humans do. Such technologies as computer vision (CV) and natural language processing (NLP) are distinguished as the largest AI areas. To imitate human vision and the ability to express meaning and feelings through language, deep learning exploits artificial neural networks that are trained on real life evidence.
While most vision-related tasks are solved using common methods nearly irrespective of target domains, NLP methods strongly depend on the properties of a given language. Linguistic diversity complicates deep learning for NLP. This chapter focuses on deep learning applications to processing the Russian language.
Network analysis as a method has applications in a wide range of fields from physics to epidemiology and from sociology to political science, and in the meantime has also reached the literary studies. Networks can be leveraged to examine intertextual relations or even artistic influences, but the main application so far has been the analysis of social formations and character interactions within fictional worlds. To make this possible, texts have to be formalized into a set of nodes and edges, where nodes represent characters and edges describe the relations between these characters in a very simple fashion: Do they or don’t they interact? Based on a selection of Russian plays and Tolstoy’s novel War and Peace, we will describe approaches to the social network analysis of literary texts.
Plagiarism currently tends to be viewed as a problem connected primarily with students, albeit more prominent authors such as William Shakespeare and George Friedrich Handel were accused of it long ago. The plagiarism continues to be widespread in educational institutions, predominantly due to single-click technology, but another contributing factor that helps make it common practice is the tolerance of plagiarism on the part of educators and academia in general. In 2004, for instance, it was estimated that 10 percent of student projects in the United States and Australia involved plagiarism (Oakes 2014, 60). By contrast, in Russia, 36 percent of respondents admitted to having regularly copied the texts of others (Kicherova et al. 2013, 2); as many as 36.7 percent of undergraduate students in 8 Russian universities took personal credit for the material they had, in fact, downloaded from the Internet
This chapter focuses on textual data that is collected for a specific purpose, which are usually referred to as corpora. Scholars use corpora when they examine existing instances of a certain phenomenon or to conduct systematic quantitative analyses of occurrences, which in turn re#ect habits, attitudes, opinions, or trends. For these contexts, it is extremely useful to combine different approaches. For example, a linguist might analyze the frequency of a certain buzzword, whereas a scholar in the political, cultural, or sociological sciences might attempt to explain the change in language usage from the data in question.
The “digital” is profoundly changing Russia today. While in the mid-1990s less than 1 percent of the Russian population had Internet access, today Russia ranks sixth globally with approximately 110 million Internet users, or three-quarters of the population (The World Factbook 2019). The proliferation of affordable smartphones in the 2010s has made Internet access a commonplace by 2020, with over 60 percent of users connecting through mobile devices, and Russia’s Internet market is the largest in Europe (GfK 2019). According to the Russian Ministry of Digital Development, Communications and Mass Media, the Russian Internet industry amounted to an estimated value of "ve trillion rubles in 2019, or 5 percent of the country’s gross domestic product (GDP) (TASS 2019). Taking into account the additional 25 million Russians who live outside of Russia, it is no surprise that Russian is the second most popular language on the Net after English (Historical trends 2019). These figures alone make Russia an attractive object for researchers interested in the development of today’s digital society. The Russian information technologies (IT) industry, moreover, is an ample provider of highly sophisticated digital tools and well-organized software solutions
Examining the “digital” as a challenge to one of the most traditional spheres of private and public life of Russians, the chapter is focused on institutional aspects of the religion digitalization in the theoretical frame of mediatization. Normatively, digitalization as such does not contradict the dogmatic teaching of any traditional for Russia religion, in Christianity, Judaism, Islam and Buddhism theologically it is being considered as a neutral process with good or bad consequences depending on human will. Therefore, functionally digital technologies are seen by religious institutions as a shaping force, one more facility (channel, tool, space, network) for effective preaching while the core of religious practices still remains based on non-mediated interpersonal communication.
Rapidly proliferating social media not only serve as a new channel of human communication but also open up research opportunities to ask a wider set of questions about political, sociological and psychological factors that influence interpersonal and group online communication, development and maintenance of personal networks, and the growth or decline of social capital. In this chapter, we discuss the research opportunities provided by the new surveys, observational and experimental data that may be obtained from a social networking site. For doing so, we refer to Russian-language social networking sites (SNS) or SNS segments, notably VKontakte as the most popular SNS in Russia. We demonstrate how the aforementioned types of data may or have already been used to address research tasks from a number of disciplines.