Translation Tutorial: From The Total Survey Error Framework To An Error Framework For Digital Traces Of Humans

FAT* '20: PROCEEDINGS OF THE 2020 CONFERENCE ON FAIRNESS, ACCOUNTABILITY, AND TRANSPARENCY(2020)

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摘要
The digital traces of hundreds of millions of people offer increasingly comprehensive pictures of both individuals and groups on different platforms, but also allow inferences about broader target populations beyond those platforms. Studying the errors that can occur when digital traces are used to learn about humans and social phenomena is essential. Many similar errors also affect survey estimates, which survey designers have been addressing for decades, most notably using the Total Survey Error Framework (TSE). In this tutorial, we first introduce the audience to the concepts and guidelines of the TSE and how they are applied by survey practitioners in the social sciences. Second, we introduce our own conceptual framework to diagnose, understand, and avoid errors that may occur in studies that are based on digital traces of humans.
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关键词
Survey Methodology, Computational Social Science, Digital Traces, Representativeness, Measurement Errors
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