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This content will become publicly available on April 30, 2024

Title: Who Broke Amazon Mechanical Turk?: An Analysis of Crowdsourcing Data Quality over Time
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.  more » « less
Award ID(s):
1816923
NSF-PAR ID:
10462328
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
WebSci '23: Proceedings of the 15th ACM Web Science Conference 2023
Page Range / eLocation ID:
335 to 345
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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    Carlson, Daniel L. and Richard J. Petts. 2022. Study on U.S. Parents’ Divisions of Labor During COVID-19 User Guide: Waves 1-2.  

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