Computational Social and Network Sciences
With the introduction and spread of new digital technologies, the penetration of the Internet and social networks into the lives of a large number of people, researchers are receiving more and more information about the actions and interactions in various social groups, teams and society as a whole. Such 'digital traces' turn into 'big data', the storage and processing of which becomes possible thanks to the development of computer computing power and the creation of advanced and fast algorithms and data analysis tools. As a result, human society – the traditional object of study of social sciences – can be viewed from a new, previously inaccessible perspective.
«We live life in the network. We check our e-mails regularly, make mobile phone calls from almost any location, swipe transit cards to use public transportation, and make purchases with credit cards. Our movements in public places may be captured by video cameras, and our medical records stored as digital files. We may post blog entries accessible to anyone, or maintain friendships through online social networks. Each of these transactions leaves digital traces that can be compiled into comprehensive pictures of both individual and group behavior, with the potential to transform our understanding of our lives, organizations, and societies».An interdisciplinary scientific field that uses rigorous computational methods to analyze and model various social processes and phenomena is called Computational social sciences. Unlike 'traditional' social sciences, which operate on the concept of sampling units of analysis, computational social sciences work with large volumes of available data – complex, rapidly changing and not clearly structured – studying the entire set of objects of interest. The use of advanced analytical tools, such as deep learning and natural language processing, allows to identify hidden patterns in human behavior, and computer modeling makes it possible to test various hypothetical situations that might occur in social systems. All together, this allows to take a fresh look at what society is and how it works.
Lazer D. et al. Computational social science //Science (New York, NY). – 2009. – Т. 323. – №. 5915. – С. 721-723.
Important information that becomes available when studying 'digital traces' is information about interactions between members of a social system, which makes it possible to study social networks of relations between various subjects belonging to different levels of analysis – people, organizations, countries, etc. Attention to relational connectivity and dependence between units of analysis is fundamental in another rapidly developing interdisciplinary field – Network science. And if the study of social relations in the concepts of nodes, connections and networks is not new in the social sciences, then it is digital technologies that allow to reach new levels of analysis of social systems, making it possible to study complex networks in dynamics and their modeling using advanced algorithms.
Research on human interactions has relied mainly on one-time, self-reported data on relationships. New technologies offer a moment-by-moment picture of interactions over extended periods of time, providing information about both the structure and content of relationships. Virtual worlds, capturing a complete record of individual behavior, offer ample opportunities for research—experimentation that would be impossible or unacceptable».The track 'Computational Social and Network Sciences' of the online master’s programme 'Data Analytics and Social Statistics' allows students to gain deeper knowledge of modern trends and theoretical and methodological developments in the field of current trends at the intersection of exact and social sciences, based on the collection and analysis of large volumes data with unprecedented breadth, depth and scale. As part of the courses, students are introduced to the mathematical apparatus and methods of statistical analysis, the use of methods and tools of computer science for collecting, processing data and modeling, as well as the use of the theoretical and methodological apparatus of social sciences to formulate research design, interpretation and presentation of results ('Programming in R and Python', 'Data Mining', 'Applied Linear Models', 'Multivariate Data Analysis', 'Unstructured Data Analysis', 'Structural Equation Modelling', etc.). A separate opportunity of the track is taking several courses on network analysis, which allow you to immerse yourself in this research area from scratch ('Introduction to SNA', 'Advanced SNA in Pajek', 'Statistical methods in Network analysis', 'Social network analysis with R', Research seminar 'Working with network data'). Students will become familiar with the implementation of network analysis using the universal programming languages R and Python, as well as in the programs Pajek, RSiena, Gephi, Orange, etc. The combination of acquired knowledge and skills is carried out within the framework of the corresponding Research seminar 'Computational Social and Network Sciences').
Lazer D. et al. Computational social science //Science (New York, NY). – 2009. – Т. 323. – №. 5915. – С. 721-723.
The track will be of interest to both students with a basic education in the social sciences and humanities, who will be able to gain knowledge in the field of working with big data and their advanced analysis, and students with a basic education in the exact sciences, who will be able to gain the skills necessary for research in fields of sociology, psychology, political science, economics, linguistics and other social sciences. The track is suitable for students with different basic education who want to develop in the field of network analysis, a new disciplinary area for Russian practice. Graduates of this track of the program will be able to work in the research industry in the field of applied social research, applying advanced methods to study various social phenomena and processes. Since the track pays special attention to design issues and the specifics of conducting social research, its graduates will be able to continue working in an academic environment, if desired, enrolling in PhD programs or graduate school.