Abstract

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.

Highlights

  • Big data and computational approaches present a potential paradigm shift in the social sciences, since they allow for measuring human behaviors that cannot be observed with survey research [1, 2, 3]

  • We focus on a third option that can enhance both automated and manual approaches to data augmentation: using online crowdsourcing marketplaces such as Amazon Mechanical Turk (MTurk)

  • We argued that online crowdsourcing as a data augmentation platform holds unique potential to add validity and value to applications of big data to social science research questions at low cost, and our content analysis suggests that researchers are beginning to use it for these purposes

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Summary

Introduction

Big data and computational approaches present a potential paradigm shift in the social sciences, since they allow for measuring human behaviors that cannot be observed with survey research [1, 2, 3]. The transformative potential of big data for the social sciences has been compared to how “the invention of the telescope revolutionized the study of the heavens” [4]. Some areas of social science have been slow to embrace big data. Lazer and Radford [5] note that only 15 of 422 articles (3.6%) published in the top journals in sociology between 2012 and 2016 contained analyses of big data. More fundamental constraints on the acceptance of big data among social scientists

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