Nathan Hahn, Joseph Chee Chang, and Aniket Kittur. CHI 2016.
BEST PAPER NOMINATION
People often search to web to find solutions to problems beyond factual question, such as planning road trips, writing an report, or buying a new camera. The Knowledge Accelerator uses crowdworkers to synthesize different information sources on the web in response to a query. We prototyped this system in order to explore crowdsourcing complex, high context tasks in a microtask environment.
Crowdsourcing offers a powerful new paradigm for onlinework. However, real world tasks are often interdependent,requiring a big picture view of the difference pieces involved. Existing crowdsourcing approaches that support such tasks – ranging from Wikipedia to flash teams – are bottleneckedby relying on a small number of individuals to maintain thebig picture. In this paper, we explore the idea that a computational system can scaffold an emerging interdependent,big picture view entirely through the small contributions ofindividuals, each of whom sees only a part of the whole. Toinvestigate the viability, strengths, and weaknesses of this approach we instantiate the idea in a prototype system for accomplishing distributed information synthesis and evaluateits output across a variety of topics. We also contribute a setof design patterns that may be informative for other systemsaimed at supporting big picture thinking in small pieces.
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