AAAI Publications, Fifth AAAI Conference on Human Computation and Crowdsourcing

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Drafty: Enlisting Users To Be Editors Who Maintain Structured Data
Shaun Wallace, Lucy Van Kleunen, Marianne Aubin-Le Quere, Abraham Peterkin, Yirui Huang, Jeff Huang

Last modified: 2017-09-21

Abstract


Structured datasets are difficult to keep up-to-date since the underlying facts evolve over time; curated data about business financials, organizational hierarchies, or drug interactions are constantly changing. Drafty is a platform that enlists visitors of an editable dataset to become ``user-editors'' to help solve this problem. It records and analyzes user-editors' within-page interactions to construct user interest profiles, creating a cyclical feedback mechanism that enables Drafty to target requests for specific corrections from user-editors. To validate the automatically generated user interest profiles, we surveyed participants who performed self-created tasks with Drafty and found their user interest score was 3.2 higher on data they were interested in versus data they had no interest in. Next, a 7-month live experiment compared the efficacy of user-editor corrections depending on whether they were asked to review data that matched their interests. Our findings suggest that user-editors are approximately 3 times more likely to provide accurate corrections for data matching their interest profiles, and about 2 times more likely to provide corrections in the first place.

Keywords


crowdsourcing; peer production; database systems; collective intelligence

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