The author devotes the article to the problem of inspecting the role and possibilities of digital sociology in the process of forming information arrays and their subsequent evaluation.
International Journal of Management (IJM) Volume 11, Issue 3, March 2020, pp 63–75, Article ID: IJM_11_03_008 Available online at http://www.iaeme.com/ijm/issues.asp?JType=IJM&VType=11&IType=3 Journal Impact Factor (2020): 10.1471 (Calculated by GISI) www.jifactor.com ISSN Print: 0976-6502 and ISSN Online: 0976-6510 © IAEME Publication Scopus Indexed THE ROLE AND POSSIBILITIES OF DIGITAL SOCIOLOGY IN THE PROCESS OF FORMING INFORMATION ARRAYS AND THEIR SUBSEQUENT EVALUATION Aleksandra Polyakova Industrial University of Tyumen, Russian Federation, Russia Corresponding Author: agpolyakova@mail.ru ABSTRACT The author devotes the article to the problem of inspecting the role and possibilities of digital sociology in the process of forming information arrays and their subsequent evaluation Socio-economic policy issues attract the greatest attention of the residents of those regions whose level of development is far from the desired, but consensus on their content, methods and mechanisms of implementation has not yet been reached One of the reasons is that the property status, material well-being and self-esteem of the Russian population depend on a significant number of factors, the effect of which is multidirectional and perceived differently by different social groups Taking into account that socio-economic policy should satisfy all or most citizens as much as possible, there is a need to measure this satisfaction quickly, including not only post factum on the basis of statistical or sociological observation, but also before making appropriate decisions All this creates the need to create tools that would allow us to assess the consequences and reaction of citizens to various information occasions for the examining the effects of intensification of efforts in the field of both economic and social policy The types of interaction determine the appearing of the main tasks, the solution of which within the monitoring system can provide the desired result In particular, scanning social networks and collecting data from them of different types is of great interest The collected data is structured as a graph in order to examine their structural properties, since the properties of graphs can be reliable indicators of human behavior Financing: The study was funded by RFBR and Kurgan oblast according to the research project № 19-411-450001/19 Keywords: digital sociology, information arrays, socio-economic policy, population http://www.iaeme.com/IJM/index.asp 63 editor@iaeme.com The Role and Possibilities of Digital Sociology in the Process of Forming Information Arrays and their Subsequent Evaluation Cite this Article: Aleksandra Polyakova, The Role and Possibilities of Digital Sociology in the Process of Forming Information Arrays and their Subsequent Evaluation, International Journal of Management (IJM), 11 (3), 2020, pp 63–75 http://www.iaeme.com/IJM/issues.asp?JType=IJM&VType=11&IType=3 INTRODUCTION Today, the Internet as a form of space is becoming more and more independent in the context of generating social interactions, as well as more open for use in a wide range of spheres of life For many, the Internet space appears as an alternative reality, but not all processes in the "physical space" can be described as "objective", since there is always a subjective distortion of the perception of information, as well as competition of interests At the same time, if the "physical" space is the object of research in a large number of areas of knowledge, then generally accepted and proven approaches to the study of the Internet space have not yet been developed, which actualizes the need to find and adapt the appropriate tools that meet the requirements of the scientific approach In this way, the well-known thesis that social relations are a reflection of economic policy can be successfully translated in relation to the Internet space, which seems to be poorly examined The connectedness of space in the" Internet reality" is higher and it will become the driver of the information cascade, whereas in the field of socio-economic policy, the problem field under consideration may not be taken into account or voiced at all MATERIALS AND METHODS The works of N Wiener and A Turing became the prologue to the conceptualization of the digital economy and, later, of sociology The concept itself became possible as a result of generalization of the achievements of D Bell, E Toffler and J A Turing Galbraith, supplemented by the theory of information networks and the methodology of network testing Examination of social networks as a research method has become widespread thanks to the research Of S Wasserman and M Granovetter., A Brandt and co-authors noted that since the early 1990s, a lot of information about network testing began to appear, but academic and political rhetoric was accompanied by an extremely small amount of analytical research The development of the problem can be found in the works of D O'Sullivan, where empirical data justify the possibility of applying key concepts and definitions from graph theory, using graph measures to study the local and global network structure, as well as special attention is paid to the structural features of the Central-level network, cohesive subgraphs and structural equivalence S Sengupta and co-authors, who developed and described a method for formalizing the relationships between actors, taking into account the characteristics of these relationships, proposed the solution to the particular problem of identifying opinion leaders From earlier works, attention should be paid to the work of M Farrugia and A Quigley, who investigated a number of ways to build networks of interaction between actors and described approaches to identifying opinion leaders In Russian science the problems of digital economy (to a lesser extent, sociology) are covered in the scientific works of A R Bakhtizin, V V Godin, S A Dyatlov, A Zuev, V L Inozemtsev, A N Kozyrev, B V Korneychuk, A B Kuritsky, V L Makarov, I V Nevolin, R M Nizhegorodtsev, T P Nikolaeva, L Myasnikova, O S Sukharev, V Yu Petrov, S I Parinov, A A Porokhovsky, I I Rodionov, I A Strelet, O S Sukhareva, V L Tambovtseva, Yu V Trifonova, A.D Ursula, M I Frida, G S Khizhi, etc A significant number of works by American, European and Russian researchers are devoted to the formation of new forms of interaction between the state and society in the conditions of digitalization A great contribution to the development of this problem was http://www.iaeme.com/IJM/index.asp 64 editor@iaeme.com Aleksandra Polyakova made by the works of such scientists As S Y Glazyev, R Dal, V V Dementyev, V E Dementyev, N D Kondratyev, M Castels, D S Lviv, V L Makarov, B Sterling, V M Polterovich, and others RESULTS In the transition to a new technological way of society, the role of digital sociology and the provisions of Industry 4.0 increases The issues of innovative development of socio-economic systems, which are closely intertwined with the problems of dynamic disequilibrium and pseudo-stability of systems, as well as their management, are among the new unsolved problems in modern science The term "digital sociology" has not yet been widely used and is only beginning to appear in scientific circulation It is first found in an article published in 2009 by D Vinn [1] Subsequently, works were published that reveal the subject of digital sociology [2] and define it as a direction that studies the use of digital media in everyday life Social monitoring was included in the subject area of digital sociology largely due to the work of N Mares [3], which resulted in the incorporation of digital technologies in the study and adjustment of social dynamics Today, this area has a theoretical framework, supported by the theories of postindustrialism, information society, digitalization, etc [4; 5; 6] Many researchers offer new interpretations of previous sociological concepts, taking into account the emergence and development of digital reality [7] At the same time, there is a lot of work to be done to expand the methodological basis, include new algorithms and technologies in the work of social processes, and develop mechanisms for their application for managing socio-economic processes [8] It is obvious that the transfer of digital sociology from theory to the space of action can provide a significant reduction in transaction costs, including those associated with obtaining information and providing public services, on the scale of the national economy Digital sociology is concerned with the work of the social environment, which is represented by a community of actors who share common interests or have other reasons for interaction As an object of scientific search, it is viewed through a social structure consisting of a group of nodes represented by social objects and the relationships between them [9; 10] Technically, it is an interactive multi-user website whose content can be changed by network participants The digital environment unfolds in social media presented by various sources A significant increase in the use of social networks has led to an increasing accumulation of data, which has been called Social Media Big Data [11] Social media sources can be represented in such forms as blogs (Blogger, LiveJournal), microblogs (Twitter, FMyLife), social networks (Facebook, LinkedIn), wikis (Wikipedia, Wetpaint), web services that allow Internet users to share, create, search and manage bookmarks (addresses) of web resources (Delicious, CiteULike), social news (Digg, Mixx), reviews and opinions (ePinions, Yelp), and multimedia sharing (Flickr, YouTube) Social media and social networks, in particular, have the greatest potential for extracting information from existing sources of information As from the point of view of achieving the goals and objectives of the examination the most interesting are the social network, let us consider the main trends associated with distributed social media, represented by social networks and their popularity and use in practical activity at creation of system of monitoring of reactions and quality of life of the population in the digital environment [12] According to ROMIR, the most popular platforms in the Russian Federation are "Vkontakte", "Facebook", "Odnoklassniki", "My world", "Instagram" Local networks hold the lead – in Contact (86%) and Odnoklassniki (75%) Facebook is on the third step of the rating The most popular and widespread network is "in contact" It is represented by young users (the share of respondents aged 18-24 is 85%) and is the leading social network in terms http://www.iaeme.com/IJM/index.asp 65 editor@iaeme.com The Role and Possibilities of Digital Sociology in the Process of Forming Information Arrays and their Subsequent Evaluation of visitor activity (45% of registered users on this portal visit it daily, and 70% - more than once a day) [13] One of the three Vkontakte users spends more than thirty minutes per visit Taking into account the presented data it is reasonable to assume that Vkontakte is the most popular social resource for a young audience Facebook and Instagram are the two main platforms that are in demand from marketers Facebook popularity is one of the trends that should be taken into account: for the first time in the last years, Facebook, as the most significant platform for marketers, has seen its share decrease from 67% in 2018 to 61% in 2019 At the same time, one in ten marketers indicated that they would continue to reduce the volume of their marketing on Facebook However, 51% of marketers plan to increase their activity on Facebook, although the share of such users decreased over the year from 62% in 2018 [13] Many of the changes involve constant modifications made by Facebook to the way the news feed is displayed, as well as news filtering systems that transform socially relevant interactions Facebook is a leader in the ranking of social media popularity among SMM specialists: it is used by 94% of marketers, which is 21 percentage points ahead of the nearest pursuer – Instagram, which is also controlled by Facebook, which allows us to conclude that one hundred percent coverage of marketers engaged in network promotion Almost two-thirds of marketers consider Facebook to be the most important information resource for online promotion Popularity of Instagram among marketers continues to grow, and the share of promotion professionals using this social platform has grown from 66% in 2018 to 73% in 2019, surpassing LinkedIn for the first time At the same time, the percentage of marketers, planning to increase activity on Instagram, much higher compared to Facebook: 69% vs 61% Other platforms are much less popular than Facebook and Instagram (Fig 1) Snapchat Messenger bots Pinterest YouTube LinkedIn Twitter Instagram Facebook 20 40 2019 60 80 100 2018 Figure The most common social media platforms (% of marketers using a given platform) Compared to 2018, there is an increase in the popularity of Instagram from 66% to 73%, a drop in Twitter from 62% to 59%, and the expansion of LinkedIn expanded from 56% YouTube grew by p p., Pinterest-by p.p., and Snapchat decreased by p p in the global community, interest in Messenger bots is decreasing: in 2019, only 14% of marketers used them and only 32% plan to increase their activity in bots, compared to 39% in 2018 However, it should be noted that there is a growing interest in YouTube – 71% of marketers plan to expand the use of videos on YouTube, and 75% want to learn more about marketing on this platform Thus, YouTube is the number one video channel used by 57% of marketers http://www.iaeme.com/IJM/index.asp 66 editor@iaeme.com Aleksandra Polyakova In General, the spread of a particular platform confirms the thesis that, first of all, network promotion professionals put improving interaction first They are interested in how to achieve better contact – this is the main question that SMM specialists are looking for an answer to In addition, there are significant differences in the prevalence of social media used in the B2B and B2C segments (figure 2) Facebook dominates the B2C space, and is chosen by the majority of marketers (69%) [14] 80% 70% 60% 50% 69% 48% 40% 30% 30% 16% 9% 20% 4% 10% 5% 3% 4% 5% 2% 1% 1% 2% 0% Facebook LinkedIn Instagram Twitter B2B YouTube Messenger Pinterest bots B2C Figure The most common social media platforms in the B2B and B2C segments (% of marketers using the specified platform) In the B2B segment, Facebook also takes a leading role, overtaking LinkedIn in terms of prevalence However, in the B2C segment, Facebook fell from 75% in 2018 to 69% in 2019 Social networks allow you to generate data arrays of various types, including text data, images, videos, sounds, and geolocation For rice shows the frequency of use of a particular type of data Figure Distribution of data types by frequency of use (% of mentions) [15] As a rule, a special approach is required for weakly structured data, since the interpretation of their incoming stream is impossible without the use of software that can use the specified language for describing such data The use of semi-structured data is positively distinguished by the fact that it contains not only the data itself, but also metadata containing information about the relationship between data and how it is formed For processing semistructured data, appropriate software products are used, which are extremely time-consuming http://www.iaeme.com/IJM/index.asp 67 editor@iaeme.com The Role and Possibilities of Digital Sociology in the Process of Forming Information Arrays and their Subsequent Evaluation to develop At the same time, the market today offers a significant number of ready-made solutions, such as converters that convert data from XML in a table display One of the most difficult tasks is processing unstructured data In this situation, it is often necessary to use manual data processing, analysis, and random verification Only the elaboration of this stage makes it possible to present them in the necessary form – but even then with the use of specialized SOFTWARE This stage requires monitoring of data types, their validity, and checking and synchronizing the measurement systems used in the model When collecting data it is also necessary to carry out their systematization and introduction of metadata With a variety of sources of unstructured information, it is often necessary to manage the collection and find a balance in the amount of information from different sources In social networks, text content is an example of unstructured data, while friend / subscriber relationships are an example of structured data Social media research is implemented on the basis of Internet technologies, which are successfully systematized in the work Of M V Kibalkin as follows: (1) special services created for solving research problems in the framework of experimental sociological research; (2) Internet services that can provide some private social characteristics of the studied phenomena, objects, resources, as well as means of obtaining primary data based on search engine operators; (3) sociological research on-line panels and platforms; (4) specialized sociological survey research services [7] Research that contains search problems is more focused on the use of special algorithms, adaptation of existing ones, or creation of proprietary algorithms that allow solving non-trivial problems in the course of experimental sociological research These include working with information cascades, which require the implementation of specific analytical procedures and the use of proprietary research algorithms Today, the Internet space, social networks, and the information cascades generated by them are increasingly being strengthened in discourse as tools of direct democracy that have a much greater potential to influence the political system than elections, referendums, and citizen gatherings However, in science and practice, the processes occurring in this environment have not received a proper scientific description and remain poorly examined The emergence of online social platforms and their use in the last decade with an exponentially increasing trend has made it possible to examine the behavior of people with an unprecedented amount of data Currently, millions of people communicate daily on social networks, where they exchange information and discuss a variety of topics Moreover, the relay of messages leads to the formation of information cascades in the Internet space According to the examination conducted by ROMIR holding together with the media congress "Commonwealth of journalists" through an online testing, representatives of the Russian media most often turn to online publications for information (70%) [16] Internet versions of print media and TV channels are the second most popular among Russian journalists (65%) New media presented only in social networks are slightly behind (50%) Journalists from the CIS also prefer online publications and Internet pages of popular publications and TV (59% each) The media in social networks shared the second position and TV channels (27% each) Data sets, each consisting of individual online activity before, during, and after an event, can be analyzed in terms of the volume of registered messages Data sets can affect any important events or phenomena that have occurred in different areas that are somehow related to socio-economic issues Each data set involves modeling user actions within a multi-layer network, where specific types of interaction are transmitted within each layer The multi-layer view makes it possible to demonstrate that different types of interaction are somehow generated by networks with different statistical properties, such as the distribution of structure and clustering degrees Online activity models cannot discard the information carried by this http://www.iaeme.com/IJM/index.asp 68 editor@iaeme.com Aleksandra Polyakova multi-layer representation of the system, and must take into account the different processes generated by different types of interactions In addition, the network itself is characterized by the presence of statistical patterns among various events, since the observed non-trivial topological patterns may represent universal features of social dynamics in social networks on the Internet during the course of certain events Thus, the behavior of an individual and their relationships in the social environment can be examined, based on the examination of social networks that demonstrate the pattern of relationships through the network structure Social networks are both the cause and the result of individual behavior: they provide and limit opportunities for individual choice The global structure of the network is determined by private decisions of individuals who initiate interactions, build, maintain or break relationships The value of the examined relations determines the network structures, and opportunities or limitations, forming a social capital that represents the totality of opportunities created by social relationships Online interactions are a good example of social interactions, and as a result, make it possible to track the activity of people in online social networks, as well as to explore the social dynamics of a person Social networks carry information about what is happening and are used to express feelings and opinions In social networks, the appearance of an actual topic leads to a temporary set of users who are currently interested in this issue Finding an influential person and measuring their influence is an interesting task in a social network First, a person who has many followers has more influence on others However, the impact assessment does not depend on the number of subscribers To extract knowledge from social network data, we use social network analysis (SNA), which examines the network graph associated with an event, process, or phenomenon This graph is based on reproducing the relationships between users who are associated with a given topic, and takes into account their reaction in the form of a response, mention, or retweet Centrality measurement and link testing are used to identify influential users As a result, an understanding of the network architecture and the role of actors in it is formed In the examining social networks, a significant number of metrics have been developed to characterize and compare network structures and authors' positions in networks Depending on what determines the differences in the link architecture, and therefore the capabilities, the examining can focus on differences in centrality, on the inspection of strongly connected clusters, authors who are structurally equivalent in networks, or vice versa, unique positions Other metrics allow you to compare network structures in general, for example, to investigate their effectiveness in achieving a goal In addition, statistical network models can be used to test the structure, evaluate parameters, or test network effects Evaluation of social networks is based on network examination, which, presented in a modern way, has a fairly deep history It is based on L Euler's graph theory It is based on the problem of the seven Konigsberg bridges, which is an old mathematical problem that asked how to walk over all seven bridges of Konigsberg without passing through any of them twice First, L Euler solved it in 1736, who proved the impossibility of passing, and invented the socalled Euler cycles as a result An Euler cycle is called such if it passes through each edge of the graph exactly once Based on this, a connected undirected graph contains an Euler cycle if and only if the number of vertices of odd degree is zero Thus, the mathematical apparatus is represented by graph theory However, their construction on big data arrays became possible only in the future with the advent of both arrays themselves, as well as tools for their collection and processing Thus, we learned to build networks on big data arrays relatively recently, and the first results became known thanks to the research Of S Wasserman and M Granovetter http://www.iaeme.com/IJM/index.asp 69 editor@iaeme.com The Role and Possibilities of Digital Sociology in the Process of Forming Information Arrays and their Subsequent Evaluation Their development was preceded by the experiment of S Milgram in 1967, who asked randomly selected citizens from the United States to pass a letter to random acquaintances whom they knew by name [17] As a result, the average path length was 5, which means that people tend to connect with each other in just a few steps This "knowledge" is reflected in pop culture and well-known knowledge (for example, "Six degrees of Kevin Bacon") Much more interesting are his conclusions about the role of intermediaries Half of the letters C Milgram that reached its goal was sent to the same three people: Mr Jacobs, Mr Brown, and Mr Jones These people were people with "high levels of connections", i.e they were social centers that united the entire network of US citizens M Granovetter (1973) would call these people bridges, "bottlenecks" of the flow of information through social networks [18] He argued that the extent to which social networks intersect depends on the strength of the connections that bind them The more similar individuals there are, the more likely they are to interact and form strong relationships That is, we trust people we know and who usually have the same experience and interest as we At the same time, in order to spread new information, it is necessary to involve loosely connected individuals who connect different social circles with quite different interests Thus, it is weak connections that play a special role in spreading new information, for example, about a new product or a reaction to a process J Brown and P Reingen [19], who found that weak connections are disproportionately more relevant for spreading new ideas and spreading between communities, have empirically proved this assumption The digital version of the classic S Milgram experiment described by P Dodds, R Muhamed, and D Watts [20] also confirms the above conclusions In a 2003 experiment, 60,000 participants were asked to forward an email to 18 recipients from several countries They found that in successful chains, the message was sent to people whose relationships the sender described as" random "and" not close", hence weak connections However, they did not find digital equivalents of individuals with "high levels of connections" and real "bridges" Thus, from the point of view of any strongly connected community, "external individuals" look like "outliers", but they are important for spreading new ideas The spread of any trend or phenomenon is associated with the spread of information through individual connectors [21], i.e socially motivated people who, through the exchange of information, act as intermediaries between people and the market and unite the world with their large network M Gladwell noted that one of the most important functions of these people is to promote information from innovators to a wider audience This is important for companies that are trying to expand their customer base from first-time users to the mass market, or for politicians who are promoting new solutions that are at an early stage Many researchers who have examined the social impact and dissemination of information through offline social networks have encountered not only computational limitations, but also many factors that can have a significant impact and distort the results of the examination They had to take into account many unproven assumptions and limited data, since the nature of people's relationships and their personal characteristics were identified based on quiz Today, thanks to technological advances and the spread of popularity of social networks, social network examination has become much more effective The relationships of people with each other are easily defined in the form of successive relationships or a "graph of friends": it has become possible to build and test multiparametric models of interaction In addition, individuals share large amounts of information about themselves and their interests on the Internet, which means that quiz is not required to determine the homogeneity of groups of people This allows us to determine whether members of the community share certain characteristics and views due to the influence of the group or whether individuals with similar views originally formed the group [21] http://www.iaeme.com/IJM/index.asp 70 editor@iaeme.com Aleksandra Polyakova A Susarla, J Oh and Ya Tan examined the structure of the YouTube network, exploring the problem of reflection and considering the social influence of users, taking into account the impact of certain factors [20] Their main conclusions were that the power of influence on YouTube comes from the centrality of the node network and that this plays a crucial role in the formation of the information cascade In the work of E Bakshi, J Hoffman, W Mason, and D Watts showed the variability of Twitter through the prism of influence, which was understood as the ability to consistently create information cascades that surpass existing ones [22] They found that individuals who have a large number of followers, being influential in the past, tend to retain their influence in the present However, forecasting on this basis is unreliable The effect of the ongoing influence is valid only "on average", but not for individuals They offer marketers not to bet on individual accounts The spread of information and the power of connections is also studied using the example of Facebook, which is still one of the largest social networks In it, users can communicate with friends via bidirectional links and follow the accounts of famous people they are interested in through unidirectional links The NewsFeed algorithm determines what content each individual user should see when scrolling through an app or website You can get a link that your friend shared yesterday and share it within your network The link exchange behavior can be considered as one of the indicators of influence Online influence is easily traced within Facebook, but there are also many external sources of influence, such as personal contact or email communication The manipulation of NewsFeed for 253 million Facebook users by E Bakshi, I Rosen, K Marlow and L Adamik revealed the role of this platform in the process of spreading the information [23] They formed two groups: one group filtered information from the news feed and only could be obtained outside of Facebook, while the other group could receive information from both inside and outside When comparing the behavior of both groups, it was found that subjects who observe their own behavior are 7.37 times more likely to share the same information than non-observers Thus, people from the friends list have some influence on an individual's behavior In addition, E Bakshi and co-authors measured the strength of the connection using several types of interaction, such as the frequency of personal communication using Facebook messages or communication using comments M Granovetter's assumption that weak connections are proportionally more important for spreading new information was again confirmed empirically Another study was conducted by E San, I Rosen, K Marlow, and T Lento on the Facebook platform and focused on the examining of the spread of events, i.e., the so-called information "infection"[25] The experiment looked at a situation where the preferences of one of the users in relation to a famous person were broadcasted in its networks Friends of the user put likes, resulting in a huge tree-like diffusion network The network obtained below contradicted the common assumption that a few people are enough to spread information in an epidemic way Facebook news feed "infestation" showed that Facebook chains are generally long, but they are not the result of a single chain reaction event Instead, they are run by a large number of users whose short chains are combined In addition, they note that the maximum length of the initial node's diffusion chain cannot be predicted based on the user's demographic characteristics or Facebook usage characteristics, including the number of friends and popularity parameters Thus, it is difficult to predict in advance, who exactly will set the trend DISCUSSION Social media related to the digital environment is characterized by a fundamental difference Unlike traditional media, they are not only effective ways to reach a large audience, but also http://www.iaeme.com/IJM/index.asp 71 editor@iaeme.com The Role and Possibilities of Digital Sociology in the Process of Forming Information Arrays and their Subsequent Evaluation provide interaction with the audience, in which information can be easily personalized, as well as providing fast real-time response to the audience Social networks are based on interaction It allows for personal communication, personalization of broadcast messages, and can be quickly updated, keeping the population up to date with rapidly changing circumstances or situations Thus, while traditional media presupposes such a format of communication as "one to many", social media tends to the format "many to many" These advantages have made it possible to create a popular communication mechanism that is becoming increasingly common in public authorities, emergency response services, etc Using the digital environment for monitoring socio-economic processes allows you to use the advantages of modern technologies and tools in public administration, as well as receive information about public opinion regarding management To understand how the dynamics of user interaction is built within each individual event, it is necessary to reconstruct the network structures that connect actors through posts, responses, and mentions in which they were either an object or a subject In various literature, building a network based on Twitter data usually involves visualizing the subscribersubscriber relationship, which, however, only captures the stated relationship, but does not adequately identify the real interaction between actors In fact, users also consume the content of hundreds of accounts that appear in the news feed, regardless of whether there is real interaction Reflecting the social structure that is generated as a result of these interactions requires building a network based on exchanges between actors derived from posts they created In particular, different types of interactions can occur in the network The user, whether an individual or an organization chooses to use social networks for many reasons, but there are three main types of their use: Broadcasting (sending) information The user can share information in the form of updates or requests with friends This application involves building social networks, since the audience must be interested in the user's statement, which means that there is a relationship between them Sending information can be one-time or repetitive The feedback request In this case, information is distributed based on a request for a problem or other information Employees of public authorities can request information from the audience in the form of reviews or comments in their messages They can also use social networks as an open channel to get a response Interaction in the process of dialogue Unlike a feedback request, this type of application encourages communication or interaction directly between the user and individual members of the social media audience The nature of the interaction determines the type of message that can be structured based on their nature: • direct communication, represented by a message addressed to a specific actor Direct communication is divided into direct communication, i.e it includes messages from the Manager to the employee and external communication; • a private message is a message or note that is not related to the research process, such as holiday greetings or other personal expressions • message about the event, including speeches, meetings, meetings, etc • information message containing opinions and positions on a specific issue; • request or request that the group members take any action, such as taking part in a vote http://www.iaeme.com/IJM/index.asp 72 editor@iaeme.com Aleksandra Polyakova The types of interaction determine the appearance of the main tasks, the solution of which within the monitoring system can provide the desired result [26; 27; 28] In particular, scanning social networks and collecting data from them of different types is of great interest [29] The collected data is structured as a graph in order to examine their structural properties, since the properties of graphs can be reliable indicators of human behavior For example, several tests show that the distribution of node degrees follows a power law, both in real and social networks It points to the fact that most social network users are often inactive and only a few key users generate most of the data and traffic In this regard, three key tasks arise, namely (1) detecting the similarity of nodes, i.e the task of assessing the degree of similarity of two network users, and (2) identifying the community, i.e the task of finding user groups (called communities) that often interact with each other and are rarely outside of their community, (3) the task of identifying users who are able to ensure that other users join the communication (event or discussion) From the point of view of the result obtained, based on the type of signals extracted, analytical tools used in monitoring systems can be classified and distinguished (1) moods, (2) emotions, (3) topics of discussion, and (4) the nature of interaction and engagement Mood is a qualitative generalization of opinions on a specific issue, and mood analysis methods are used to obtain such information in a computational way Emotional analysis provides another stream of information of a qualitative nature, showing the attitude of users to a specific event Topic examination is a process that extracts topics that lie in the user's area of interest The volume of posts allows us to draw conclusions about the nature of interaction, involvement and public participation in the event You can create and use more specific signals and identify preferences, intentions, and consequences Sentiment examination used to assess public opinion about a particular social problem Since users are quite free to express their opinions, the ability to examine opinions has attracted the attention of many researchers Their approaches differ in terms of feature sets, machine learning algorithms, and text processing methods In addition, the examination is differentiated due to the fact that the user's opinion can be expressed in various ways (emoticons, hashtags, specialized vocabulary, etc.), which causes the use of various methods of processing text data, such as deleting stop words, identifying words by templates, and deleting punctuation Emotion examination, which aims to identify emotions in messages and can provide valuable information about the public's opinion on a specific issue As a rule, the examination is based on the selection of key categorical emotions based on hashtags of emotions, vocabulary of emotions, parts of speech, etc researchers also address the definition of the relationship between the emotions expressed by users, as well as their sphere of interest Thematic examination, which is one of the most important in the field of extracting information covering semantically significant topics from social media content Extracting topics in the context of social media examination helps you understand the subtopics associated with an event and which aspects of the problem have attracted the most public attention This helps to understand better the underlying dynamics of the relevant discussions, and to identify the relationship between user interests, mood, and changes in the topics learned In addition, it helps to solve classification problems Interaction examination allows you to identify the user's involvement in the monitored event Generally, the larger the data set, the more accurate and consistent the model is, because it minimizes the likelihood of systematic error Qualitative examination requires a large amount of source information and its high accuracy, which, in turn, allows implementing a semantic filtering mechanism that selects and uses a domain-specific knowledge graph http://www.iaeme.com/IJM/index.asp 73 editor@iaeme.com The Role and Possibilities of Digital Sociology in the Process of Forming Information Arrays and their Subsequent Evaluation CONCLUSIONS Systematizing the conclusions of previous studies, it is worth noting that the power of influence is not shared equally between network participants It can be described as a function of the number of links in a social network (number), as well as the strength of each link (quality) Many people's knowledge may not be sufficient if your relationship does not have high quality characteristics So working with influencers who have hundreds of thousands of followers is not in itself convincing, regardless of whether they are fake or real The big attraction is the quality of their relationship with fans, which is more important than the broad audience of individuals who "don't care" It should also be noted that the choice of data collection method can have a significant impact on the final result In fact, the data obtained through search algorithms tend to rerepresent Central actors without providing an adequate picture of peripheral activity, with a more significant bias for the network of mentions REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] Wynn J (2009) Digital sociology: emergent technologies in the field and the classroom., Sociological Forum, 24(2), 448−456 Neal R (2010), Expanding sentience: introducing digital sociology for moving beyond buzz metrics in a world of growing online socialization; Orton-Johnson K and Prior N (eds) (2013) Digital sociology: critical perspectives, Houndmills: Palgrave Macmillan Marres N (2017), Digital sociology: The Reinvention of Social Research, Polity, Cambridge Kuznetsova, I.G et al (2020) The impact of human capital on engineering innovations International Journal of Emerging Trends in Engineering Research, 8(2), 333-338 DOI: 10.30534/ijeter/2020/15822020 Tkachenko, E., Rogova, E., Bodrunov, S., Dmitriev, N (2019) Valuation of intellectual capital in the context of economic potential of A company Paper presented at the Proceedings of the European Conference on Intellectual Capital, 2019-May, P 303-314 Voronkova, O.Y et al (2020) Corporate social responsibility of business as a factor of regional development Entrepreneurship and Sustainability Issues, 7(3), 2170-2180 DOI: 10.9770/jesi.2020.7.3(47) Kibakin M V., Grishaeva S A (2019), Actual problems of digital social reality reflection: rethinking scientific concepts Digital sociology Vol No Pp 4-9 Gerasimov, V et al (2019) Control in the human capital management system in the strategy of innovative development of a region Entrepreneurship and Sustainability Issues, 7(2), 1074-1088 DOI: 10.9770/jesi.2019.7.2(20) Kolmakov, V., Polyakova, A (2019) Regional free cash flow dataset: An approach to regional performance evaluation Data in Brief, 25 DOI: 10.1016/j.dib.2019.104175 Turgaeva, A.A., Kashirskaya, L.V., Zurnadzhyants, Yu.A., Latysheva, O.A., Pustokhina, I.V., Sevbitov A.V (2020) Assessment of the financial security of insurance companies in the organization of internal control Entrepreneurship and Sustainability Issues, 7(3), 2243-2254 DOI: 10.9770/jesi.2020.7.3(52) Stefan Stieglitz, Milad Mirbabaie, Björn Ross, Christoph Neuberger (2018) Social media analytics–Challenges in topic discovery, data collection, and data preparation International Journal of Information Management 39 156-168 doi.org/10.1016/j.ijinfomgt.2017.12.002 Kolmakov, V., Polyakova, A., Polyakov, S (2019) A valuation approach to the Russian liberal establishment consolidation Administratie Si Management Public, 2019(32), 93107 DOI: 10.24818/amp/2019.32-07 http://www.iaeme.com/IJM/index.asp 74 editor@iaeme.com Aleksandra Polyakova [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] ROMIR research holding [Electronic resource] URL: https://romir.ru/studies/socialnosetevaya-jizn Stelzner M A (2009), Social media marketing industry report How Marketers Are Using Social Media to Grow Their Businesses May 2019 Электронный ресурс – Режим доступа https://www.socialmediaexaminer.com/report2019/ The Data Warehousing Institute ROMIR Research Holding Journalists prefer to get information on the Internet [Electronic resource] - URL: https://romir.ru/studies/jurnalisty-predpochitayut-poluchatinformaciyu-v-internete Milgram, S (1967) The Small-World Problem, Psychology Today, Vol (1), 61– 67 Granovetter, Mark S (1973) The Strength of Weak Ties, American Journal of Sociology, Vol 78 (6), 1360–1380 Brown, Jacqueline Johnson and Peter H Reingen (1987) Social Ties and Word-of-Mouth Referral Behavior, Journal of Consumer Research, Vol 14 (3), 350–362 Dodds, Peter Sheridan, Roby Muhamad, and Duncan J Watts (2003) An Experimental Study of Search in Global Social Networks Science, 301, 827–829 Manski, Charles F (1993), Identification of Endogenous Social Effects: The Reflection Problem The Review of Economic Studies, Vol 60 (3), 531-542 Susarla, Anjana, Jeong-Ha Oh, and Yong Tan (2011), Social Networks and the Diffusion of User-Generated Content: Evidence from YouTube Informaion Systems Research, Articles in Advance, 1–19 Bakshy, Eytan, Jake M Hofman, Winter A Mason, and Duncan J Watts (2011) Everyone’s an Influencer: Quantifying Influence on Twitter Proceedings of the 4th ACM International Conference on Web Search and Data Mining (WSDM 2011) González-Bailón S, Wang N, Rivero A, Borge-Holthoefer J, Moreno Y (2014), Assessing the bias in samples of large online networks Soc Net 38:16–27 doi: 10.1016/j.socnet.2014.01.004 Sun, Eric, Itamar Rosenn, Cameron A Marlow, and Thomas M Lento (2009) Gesundheit! Modeling Contagion through Facebook News Feed Proceedings of the Third International ICWSM Conference (2009), 146-153 Rosa, A.T.R., Pustokhina, I.V., Lydia, E.L., Shankar, K., Huda, M (2019) Concept of electronic document management system (EDMS) as an efficient tool for storing document Journal of Critical Reviews, 6(5), 85-90 DOI: 10.22159/jcr.06.05.14 Zakieva, R.R et al (2019) The significance of studying legal protection of rights on innovative creations: The case of entrepreneurship education Journal of Entrepreneurship Education, 22 (3) Herningsih, Tchuykova, N.M., Shankar, K., Wekke, I.S., Qadri, M.N (2019) The Starbucks effect: It affects on nearby organization Journal of Critical Reviews, 6(5), 160165 DOI: 10.22159/jcr.06.05.28 Zavyalov, D.V., Saginova, O.V., Zavyalova, N.B (2017) The concept of managing the agro-industrial cluster development Journal of Environmental Management and Tourism, 8(7), 1427-1441 DOI: 10.14505/jemt.v8.7(23).12 Untari, Riza Fachrizal, Nova Suryawati Monika, Rosa Delima Pangaribuan, Diana Sri Susanti and Nurcholis, (2019), Rural Sociology of Urgent Farmers by Local and Non Local People in Muting District of Merauke International Journal of Civil Engineering and Technology, 10(1), pp 707–714 http://www.iaeme.com/IJM/index.asp 75 editor@iaeme.com .. .The Role and Possibilities of Digital Sociology in the Process of Forming Information Arrays and their Subsequent Evaluation Cite this Article: Aleksandra Polyakova, The Role and Possibilities. .. http://www.iaeme.com/IJM/index.asp 65 editor@iaeme.com The Role and Possibilities of Digital Sociology in the Process of Forming Information Arrays and their Subsequent Evaluation of visitor activity (45% of registered... Possibilities of Digital Sociology in the Process of Forming Information Arrays and their Subsequent Evaluation Their development was preceded by the experiment of S Milgram in 1967, who asked randomly