Big Data’s Little Brother: Enhancing Big Data in the Social Sciences with Micro-task Marketplaces

• In Event: 038 - Section on Methodology Paper Session. Advancement in Observing and Modeling Social Processes

Abstract

Many prominent articles have claimed that “Big Data”, a catchall term for digital and other new forms of data and new approaches to data collection and analysis, will fuel a revolution in the social sciences. Advances in statistics, computer science, and electronics provide infrastructure for processing and analyzing such large quantities of data. However, sociologists and other social scientists have more slowly adopted these data sources and approaches than those in other fields, in part because many traditional social scientists remain skeptical of Big Data. Skeptics challenge Big Data as unreliably measured, decontextualized, and lacking content validity for drawing meaningful conclusions. In this paper, we present a framework for adding value and veracity to Big Data by augmenting it with crowdsourced data from micro-task marketplaces like Amazon Mechanical Turk. Such an approach can be used, for instance, to verify machine coding of the academic discipline of collaborating scientists, to find official ecumenical links between religious organizations, or to gather data on the mental health facilities of colleges and universities. Utilizing these three diverse empirical case studies, we offer a careful consideration of the costs and benefits of augmenting Big Data, as well as a how-to manual on best practices. As part of this enterprise, we offer a standardized reporting template and other tools that will enhance reproducibility. This study expands the use of micro-task marketplaces from their current use for surveys and experiments in order to expand the potential acceptance of Big Data in the social sciences.

Authors

• Nathaniel D Porter, Pennsylvania State University

• Ashton M. Verdery, The Pennsylvania State University

• S. Michael Gaddis, Pennsylvania State University