No abstract

Explores the potential of a dynamic data analysis approach to study user behavior in social networks. Currently, information appears on social networks that allows differentiating user groups by their activity within the technical capabilities of a particular social network. The description of the information field of Tomsk is presented, a brief analysis is given. A dynamic approach to the study of user behavior, the structure of nodes and connections of social networks makes it possible to identify the rate of growth or decrease in the size of the network, the redistribution of connections between groups. There are four main stages in the analysis of social networks: 1) data collection; 2) selection of data for analysis; 3) selection and application of the analysis method; and 4) drawing conclusions. To obtain a complete picture of the information field of the Tomsk region, posts for 2019 were unloaded from all regional communities. All posts were classified based on training sample and specialized machine learning algorithm.

The nature of the social influence of media on social processes, the production of virtual information practices, to study these processes currently actualizes the need to use modern new tools for collecting, processing and data analysis methods. The purpose of this work is to analyze the activity of university graduates in communities, their identification through the collection of data from social networks. Assessment of the activity of graduates in social networks was carried out by “downloading” messages and news from online university communities. For each message, activity labels (“likes”, reposts, comments) were collected and graduates of these universities were identified (reconciliation with the register of graduates was carried out). The focus of the analysis is on identifying the actions of graduates - loyalty in the media space and the dissemination of information about the university community. The main methodological guideline was the approach within the framework of the microsociological paradigm, in particular, the idea of symbolic interactionism. The heuristic potential of using big data to analyze the activity of university graduates in communities allows us to expand our methodological arsenal and overcome the limitations of existing traditional methods of collection and analysis. The main research methods: interface programming, social network analysis of user interaction in social media, Web-crawling using a search engine, statistical data processing. Results: the main digital strategies of university graduates are characterized by the expansion of the audience, the promotion of content caused by the interest of users depending on the focus of the group. Four types of alumni communities have been distinguished: groups that identify with social development, with charity, with scientific research, and education. The high average value of the activity index belongs to charitable foundations, followed by the community of culture and science. The lowest average value of the activity index is recorded in educational communities.

In the article, social media is analyzed through the focus of understanding the latter as a virtual space of the media, which reflects such identifying indicators of users as: interest, desire, enthusiasm and direction of integration processes. The relevance of the study is determined by the insufficient development and inconsistency of the concepts and empirical results of research on the processes of differentiation of the community of social networks and their role in the conditions of distance education. The methods of philosophical, analysis and hermeneutics were used: interpretation, conceptualization, comparative analysis. As a theoretical and methodological base, we used the categorical apparatus of social philosophy, mathematics, theory of practice, pragmatism, social epistemology. We used approaches to extracting the activity of user groups in a multi-layer social network: 1) extracting groups in each layer separately, and then combining communities throughout the layers; 2) first transforming the social network into one layer, and then searching for different groups within.