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- W2731291282 abstract "Manual content analysis, also known as hand-coding or annotation, is often the only way to reliably identify important social phenomena in textual data. However, it is extremely time consuming, often requiring large teams of trained undergraduate research assistants working over several years. This article presents a new approach to manual content analysis (and software) enabling researchers to enlist the help untrained citizen scientists and crowd workers to label text through the internet. After first describing the dilemma of large-scale text analysis, the article explains how hand-coding for text units of analysis (TUAs) allows the work of a hand-coding project to be decomposed into micro-tasks fit for untrained crowd workers. The approach and software are explained in detail. Then, the article compares the new approach to similar fully-manual or fully-automated approaches, finding that it is less costly and four times faster than traditional manual content analysis, while producing richer and more transparent data than other approaches. The article then outlines a number of projects that might benefit from crowd content analysis, demonstrating its generalizability to an array of social science fields. And finally, it closes with a discussion of the specific limitations and general promise of this new approach." @default.
- W2731291282 created "2017-07-14" @default.
- W2731291282 creator A5080676959 @default.
- W2731291282 date "2016-01-01" @default.
- W2731291282 modified "2023-09-27" @default.
- W2731291282 title "A Crowd Content Analysis Assembly Line: Annotating Text Units of Analysis" @default.
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- W2731291282 doi "https://doi.org/10.2139/ssrn.2808731" @default.
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