Towards Measuring Fine-Grained Diversity Using Social Media Photographs

Abstract

Diversity is an important socio-economic construct that influences multiple aspects of human lives from the prosperity of a city to corporate earnings and from criminal justice to health and social engagement. Large, heavily populated urban areas can be highly diverse at the city or even neighborhood level, but we know very little about how much people from diverse demographics (such as age and race) interact with each other. Previous work has shown that photos are important in social relationships. The growing presence of photos online and on social media, therefore presents a unique opportunity to study diversity in interactions. In this paper, we explore a novel approach to measure p-diversity, that is, a personal, photo-level diversity metric computed using social media data. Specifically, we focus on Instagram photos of multiple people interacting, and employ automatic methods for race, age, and gender estimation to quantify mixing in such photos. We compare and contrast this new measure of diversity with traditional (that is, census-based) metrics using a dataset for New York City. Results obtained motivate the use of social media photos to complement census data to develop cheaper, faster, mechanisms for studying diversity and applying them in social, economic, political, and urban planning contexts.