Cyberspace Analytics

Entry requirements: Basic terminology of data processing. Analytical skills.

Credits: 3

Course: Elective

Language of the course: Russian

Lecturer

Nikolay Butakov

Objectives

In this course students will get acquainted with general and specific definitions and key tools for gathering, analyzing and describing network data.
Learning outcomes:

  • To use methods of decision-making in non-standard conditions;
  • To find a solution appropriate for the given analytical task keeping in mind ethical and other norms. To select technologies for the
  • given task on the basis of efficiency criteria and material expenses;
  • To prepare technology synthesis projects;
  • To gather and analyze network data.

Contents

  • Introduction to the social network analysis (SNA): Basic concepts of graph theory used in network analysis, theoretical foundations and definitions of network analysis, basic principles of network analysis, alternatives to network analysis, SNA areas of application, types of data in use, ethical issues of network analysis.
  • Network metrics: Types of metrics and network properties, egocentric networks, network structures, examples, principles, complex networks, topology, scale-free network, small world.
  • Egocentric network analysis: Ego-network analysis. Actor network metrics. Centricity index (degrees of node input and output, betweenness centrality, closeness centrality, etc), tools for ego-network analysis.
    Sociocentric network analysis: Network properties. Centralization, homogeneity, multiplicity, reciprocity, transitivity, coupling force, network density, segmentation – clicks, clustering coefficients, tools for analysis of social network properties.
  • Data sources, visualization: Matrix and graphical forms of representing social network, open data sources, approaches to data gathering, tools for gathering, storing, processing and analysis of social network data, exploratory data analysis, approaches and tools of visualizing social network data.
  • Processes in networks: Dissemination of information, dynamics and evolution of social networks, feedback, types of social processes based on networks; temporal networks, dynamics of network communities, factors and indicators of structural changes, tools for analysis of network processes.
  • Content analysis: Quantitative and qualitative approaches to content analysis, objectives of content analysis, link between network and content analysis, text as network, types of texts, factors and indicators used in content analysis, text processing procedure – preparation, processing, analysis, tools for text analysis, sentiment analysis, ethical and regulatory issues of content analysis.
  • Semantic analysis: Definitions of semantic analysis, approaches to semantic analysis, theoretical foundation of semantic analysis, semantic networks: types, dimensionality, homogeneity, hierarchical relations between definitions, topic modelling, link between semantic analysis and social network analysis, tools for semantic analysis.

Format

Lectures and practical sessions

Assessment

Attendance is mandatory.