Some claim that “Big Data” will fuel a revolution in the social sciences, while skeptics challenge Big Data as unreliably measured, decontextualized, and lacking content validity. We argue that Big Data projects can be enhanced through data augmentation with crowdsourcing marketplaces like Amazon Mechanical Turk (MTurk). Following a content analysis of academic applications of MTurk, we present three empirical cases to illustrate the strengths and limits of crowdsourcing and address social science skepticism. The case studies use MTurk to (1) verify machine coding of the academic discipline of dissertation committee members, (2) link online product pages to an online book database, and (3) gather data on mental health resources at colleges. We consider the costs and benefits of augmenting Big Data with crowdsourcing marketplaces and provide guidelines on best practices. We also offer a standardized reporting template that will enhance reproducibility. This study expands the use of micro-task marketplaces to enhance social science acceptance of Big Data.
['Nathaniel D. Porter', 'Ashton M. Verdery', 'S. Michael Gaddis']
Big Data's Little Brother: Enhancing Big Data in the Social Sciences with Micro-Task Marketplaces