Project leader: Bence Ságvári
Today large amounts of data are available to research on human behaviour, and the industry that relies on collecting, combining, selling and analysing digital footprints is developing with lightning speed. Such data can increasingly be used to address those “classic” societal issues that have been in the focus of social science for more than a century. Also, the advances in the use of such data in social sciences offer the possibility to answer questions that were beyond research in the past due to the lack of available data.
The objective of our research is to achieve new results in the recently institutionalizing interdisciplinary field of computational social sciences. Our aim is to develop new and innovative methodologies for the investigation of the complex structures of social inequalities, and to explore previously unknown or non-researchable correlations. The uniqueness of the research lies in the close cooperation of social and natural scientists, in the unique datasets that we have access to (i.e. online social networks, mobile phone calls data, public procurements, etc.), and in the applied methods.
The aim of the research is to explore social inequalities from an interdisciplinary approach, through the collaboration of social and natural scientists. Our research relies primarily on network-type Big Data sources, and we focus on the following three topics: (1) Regional social and economic inequalities and patterns of migration will be explored through the analysis of large-scale databases of social networks. (2) We test network models on data of public procurements in order to discover corruption risks, and find relationship between social capital, network density and regional economic development. (3) Using large scale network data, we link social inequalities to homophilous community networks.