BIG DATA IN RESEARCH OF MIGRATION PROCESSES
The lecture series was delivered by professors E. Brodovskaya and A. Dombrovskaya in 2018-2019 in real and online regime. It was prepared for training specialists on the processes of migration, social adaptation and communication of migrants: how to use big data for the examination of social and political processes on the bases of digital markers. The collection of lectures presents the theoretical and methodological aspects, as well as the heuristic possibilities of big data as an analysis method. The manual aims to develop the skills of using cybermetry in the study of digital markers of multifactorial humanitarian processes. Different methods of smart search in Internet content are demonstrated (Predictor Mining, Data Mining), and the heuristic and methodological foundations of social computing methods are analyzed. It also provides a frame for applying the various cybermetry tools, including online services for monitoring social media, graphical and verbal interpretation of data etc. Recommended for undergraduate, graduate and postgraduate students in the fields of Political Science, Migration Studies etc., as well as the university lecturers specialized on the role of the Internet in contemporary realities, the problems of Internet communication using cybermetric analysis.
This publication was prepared and published with the support of the Erasmus+ Programme of the European Union, in the frame of Jean Monnet Activities / Jean Monnet Centre of Excellence. The title of the project: “Advancing the Systematic Learning Approach for Migrants and Disadvantaged Groups: A Unified Model for Training of Trainers (ASyLUM)”.
Project №: 575510-EPP-1-2016-1-RU-EPPJMO-CoE; Grant Agreement: 2016-2856/001-001
The European Commission's support for the production of this publication does not constitute an endorsement of the contents, which reflect the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein.