Who Broke Amazon Mechanical Turk?: An Analysis of Crowdsourcing Data Quality over Time

C. Marshall, Partha S.R. Goguladinne, Mudit Maheshwari, Apoorva Sathe, F. Shipman
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引用次数: 3

Abstract

We present the results of a survey fielded in June of 2022 as a lens to examine recent data reliability issues on Amazon Mechanical Turk. We contrast bad data from this survey with bad data from the same survey fielded among US workers in October 2013, April 2018, and February 2019. Application of an established data cleaning scheme reveals that unusable data has risen from a little over 2% in 2013 to almost 90% in 2022. Through symptomatic diagnosis, we attribute the data reliability drop not to an increase in bad faith work, but rather to a continuum of English proficiency levels. A qualitative analysis of workers’ responses to open-ended questions allows us to distinguish between low fluency workers, ultra-low fluency workers, satisficers, and bad faith workers. We go on to show the effects of the new low fluency work on Likert scale data and on the study's qualitative results. Attention checks are shown to be much less effective than they once were at identifying survey responses that should be discarded.
谁弄坏了亚马逊土耳其机器人?:众包数据质量随时间的分析
我们提出了2022年6月进行的一项调查的结果,作为研究亚马逊土耳其机器人最近数据可靠性问题的视角。我们将此次调查的糟糕数据与2013年10月、2018年4月和2019年2月在美国工人中进行的相同调查的糟糕数据进行了对比。应用既定的数据清理方案表明,不可用数据已从2013年的略高于2%上升到2022年的近90%。通过对症诊断,我们将数据可靠性的下降不是归因于恶意工作的增加,而是归因于英语熟练程度的连续性。通过对员工对开放式问题的回答进行定性分析,我们可以区分低流利度员工、超低流利度员工、满足者和不诚实的员工。我们继续展示新的低流利度工作对李克特量表数据和研究的定性结果的影响。注意检查在确定应该丢弃的调查回答方面显示出比以前有效得多。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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