Public survey instruments for business administration using social network analysis and big data

Purpose: The subject matter of this research is closely intertwined with the scientific discussion about the necessity of developing and implementing practice-oriented means of measuring social well-being taking into account the intensity of contacts between individuals. The aim of the research is to test the toolkit for analyzing social networks and to develop a research algorithm to identify sources of consolidation of public opinion and key agents of influence. The research methodology is based on postulates of sociology, graph theory, social network analysis and cluster analysis. Design/Methodology/Approach: The basis for the empirical research was provided by the data representing the reflection of social media users on the existing image of Russia and its activities in the Arctic, chosen as a model case. Findings: The algorithm allows to estimate the density and intensity of connections between actors, to trace the main channels of formation of public opinion and key agents of influence, to identify implicit patterns and trends, to relate information flows and events with current information causes and news stories for the subsequent formation of a "cleansed" image of the object under study and the key actors with whom this object is associated. Practical Implications: The work contributes to filling the existing gap in the scientific literature, caused by insufficient elaboration of the issues of applying the social network analysis to solve sociological problems. Originality/Value: The work contributes to filling the existing gap in the scientific literature formed as a result of insufficient development of practical issues of using analysis of social networks to solve sociological problems.peer-reviewe

Design of a socio-economic processes monitoring system based on network analysis and big data

Socio-economic policy should satisfy the interests of the society as much as possible and contribute to improving the quality of life. This actualizes the role of developing the theoretical and methodological foundations for building an innovative information system for monitoring the socio-economic situation and population responses. The study built and tested an algorithm for supporting management decisions based on the collection of large data sets of socio-economic information based on the principles of the digital economy and processing them through network analysis. The algorithm is focused on building a monitoring system that presupposes a synergy of the authorities and the society, not only in its pensionary part, but also among the masses, which are diverse in their representativeness. The result of the study was the formation of a theoretical and methodological framework for creating a system for making management decisions and assessing the effectiveness of the activities of government bodies, based on the principles of reflection of the final beneficiaries of economic policy.peer-reviewe

Actor identification in implicit relational data sources

Large scale network data sets have become increasingly accessible to researchers. While computer networks, networks of webpages and biological networks are all important sources of data, it is the study of social networks that is driving many new research questions. Researchers are finding that the popularity of online social networking sites may produce large dynamic data sets of actor connectivity. Sites such as Facebook have 250 million active users and LinkedIn 43 million active users. Such systems offer researchers potential access to rich large scale networks for study. However, while data sets can be collected directly from sources that specifically define the actors and ties between those actors, there are many other data sources that do not have an explicit network structure defined. To transform such non-relational data into a relational format two facets must be identified - the actors and the ties between the actors. In this chapter we survey a range of techniques that can be employed to identify unique actors when inferring networks from non explicit network data sets.We present our methods for unique node identification of social network actors in a business scenario where a unique node identifier is not available. We validate these methods through the study of a large scale real world case study of over 9 million records.</p