通过众包增强社会科学中的大数据：数据增强实践、技术和机遇
Enhancing Big Data in the Social Sciences with Crowdsourcing: Data Augmentation Practices, Techniques, and Opportunities
摘要：
大数据的重要性是社会科学家之间有争议的话题。支持者声称它将推动一场研究革命，但怀疑论者质疑它是不可靠的测量和脱离背景的，对准确回答社会科学研究问题的效用有限。我们认为，社会科学家需要有效的工具来量化大数据的测量误差，并扩大与之相关的上下文信息。许多领域的标准研究工作已经通过数据增强来实现这些目标，即对已知数量的测量进行系统评估，并通过添加新信息来扩展现有数据。传统上，这些任务是使用训练有素的研究助理或专门的算法完成的。然而，这种方法可能无法扩展到大数据或安抚其怀疑论者。我们考虑第三种可能增加大数据的有效性和价值的替代方案：在线众包的数据增强。我们提出了三个实证案例，以说明众包的优势和局限性的学术研究，特别是着眼于如何将它们应用于数据增强任务，将加快社会科学家接受大数据。这些案例使用Amazon Mechanical Turk来1.验证学位论文委员会成员的学科自动编码，2.将在线产品页面链接到图书数据库，以及3.收集大学心理健康资源的数据。根据这些案例，我们考虑了通过众包市场增强大数据的成本和收益，并提供了最佳实践指南。我们还提供了一个标准化的报告模板，这将提高可重复性。
英文摘要：
The importance of big data is a contested topic among social scientists. Proponents claim it will fuel a research revolution, but skeptics challenge it as unreliably measured and decontextualized, with limited utility for accurately answering social science research questions. We argue that social scientists need effective tools to quantify big data's measurement error and expand the contextual information associated with it. Standard research efforts in many fields already pursue these goals through data augmentation, the systematic assessment of measurement against known quantities and expansion of extant data by adding new information. Traditionally, these tasks are accomplished using trained research assistants or specialized algorithms. However, such approaches may not be scalable to big data or appease its skeptics. We consider a third alternative that may increase the validity and value of big data: data augmentation with online crowdsourcing. We present three empirical cases to illustrate the strengths and limits of crowdsourcing for academic research, with a particular eye to how they can be applied to data augmentation tasks that will accelerate acceptance of big data among social scientists. The cases use Amazon Mechanical Turk to 1. verify automated coding of the academic discipline of dissertation committee members, 2. link online product pages to a book database, and 3. gather data on mental health resources at colleges. In light of these cases, 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.
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