Studying Socioeconomic Inequality from Digital Footprint Networks

Abstract

Social connectivity structures and reinforces inequality in society but also provides a footprint of it. I will present two projects in which we analyze network structures to extract information about socioeconomic inequality. In the first project, we use correspondence analysis to infer Twitter users’ socioeconomic status from the accounts of commercial and entertainment brands in the US they follow. In the second project, we analyze SafeGraph data on physical store visits and co-visits in the US to investigate nuances of socioeconomic inequality in daily consumption. The projects demonstrate how we can both exploit and examine traces of economic and cultural consumption practices to understand an important manifestation of inequality in everyday life.