Project title: Social and philosophical analysis of well-being images based on social networks — Quality of Life — TSU

Project title: Social and philosophical analysis of well-being images based on social networks

Project manager: Evgeniy V. Shchekotin, Research Fellow, Laboratory of Big Data in Social Sciences, TSU

Main executives:

  • Alexey A. Baryshev, Research Fellow, Laboratory of Big Data in Social Sciences, Tomsk State University
  • Julia O. Mundrievskaya, Postgraduate Student of TSU

Abstract:

The relevance of the study is due to the great theoretical and practical interest in the study of the problem of well-being and quality of life of the population at different levels of social community (personality, social group, population, etc.)

The Study of various aspects of this problem is of great applied importance, because in recent years improving the quality of life of the population is one of the leading priorities of the state development strategy, both at the regional and Federal levels.

 The aim of the project is to study images (metaphors) of well-being through which people manifest their ideas about «good life».

Toidentify the fundamental metaphors of well-being, it is proposed to carry out a paradigm analysis of the theories of well-being and the quality of life that currently exist. The paradigm metaphors are based on different approaches to the interpretation of the category «life» of various disciplinary fields and matrices. On the basis of the selected paradigms-metaphors, an analytical model of personal well-being will be developed.

This model is used for the study of specific images of well-being that individuals design into practical daily activities. In order to collect information on specific well-being patterns by which individuals represent personalized perceptions of well-being («good life»), methods of social networking and big data analysis are used. Based on the study of social media materials (photos, video content, text messages, etc.), various systems of well-being perceptions will be classified.