Our understanding about the structure and dynamics of social systems has been developed considerably during the last years due to the recent availability of large digital datasets collecting interactions of millions of individuals. However, although these studies consider the structural, temporal, or spatial characters of human interactions they commonly miss one important dimension regarding the socioeconomic status of individuals, which may largely determine the social structure itself. The uneven distribution of wealth and individual economic capacities are among the main forces, which shape modern societies and arguably bias the emerging social network. In the first part of this talk, we will discuss a set of results aiming to close this gap through the analysis of coupled datasets recording the mobile phone communications and bank transaction history of one million anonymised individuals living in a Latin American country. We show that wealth and even debt are unevenly distributed among people, who in turn are connected into a strongly stratified structure including tightly connected ``rich clubs’’. Subsequently, we will analyse a dataset combining a large French Twitter corpus with detailed socioeconomic maps obtained from national census, professional occupation data, and socioeconomic data remotely sensed from satellite images. We show how key linguistic variables measured in individual Twitter streams depend on factors like socioeconomic status, location, time, and the social network of individuals.

Márton Karsai, PhD, HDR, is an Associate Professor in Network and Data Science at the Central European University, member of the IXXI Complex System Institute and fellow of the ISI Foundation. His research interest falls within human dynamics, computational social science, and data science, especially focusing on heterogeneous temporal dynamics, spatial and temporal networks, socioeconomic systems and social contagion phenomena. His main expertise are in analysing large human interaction datasets and in the development of data-driven models of various social phenomena.