Enhancing big data in the social sciences with crowdsourcing: Data augmentation practices, techniques, and opportunities

Nathaniel D. Porter, Ashton M. Verdery, S. Michael Gaddis

Research output: Contribution to journalArticle

Abstract

Proponents of big data claim it will fuel a social research revolution, but skeptics challenge its reliability and decontextualization. The largest subset of big data is not designed for social research. Data augmentation-systematic assessment of measurement against known quantities and expansion of extant data with new information-is an important tool to maximize such data's validity and research value. Using trained research assistants or specialized algorithms are common approaches to augmentation but may not scale to big data or appease skeptics. We consider a third alternative: data augmentation with online crowdsourcing. Three empirical cases illustrate strengths and limitations of crowdsourcing, using Amazon Mechanical Turk to verify automated coding, link online databases, and gather data on online resources. Using these, we develop best practice guidelines and a reporting template to enhance reproducibility. Carefully designed, correctly applied, and rigorously documented crowdsourcing help address concerns about big data's usefulness for social research.

Original languageEnglish (US)
Article numbere0233154
JournalPloS one
Volume15
Issue number6
DOIs
StatePublished - Jun 2020

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

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