Can AI help in crowdsourcing? testing alternate algorithms for idea screening in crowdsourcing contests

Working paper

Crowdsourcing, while a boon to ideation, generates thousands of ideas. Screening these ideas to select a few winners is a major challenge because of the limited number, expertise, objectivity, and attention of judges. This paper compares original and extended versions of three recently published theory-based algorithms from marketing to evaluate ideas in crowdsourcing contests: Word Colocation, Content Atypicality, and Inspiration Redundancy. Each algorithm suggests predictors of winning idea...

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Authors: Bell, J; Pescher, C; Tellis, GJ; Fuller, J

• Peer review status: Not peer reviewed
• Publisher: University of Oxford
• Publication date: 2020-12-17
• Language: English
• Keywords: nature language processing; statistical learning; network theory; SBTMR; crowdsourcing; prototypicality; creativity