The proposed tutorial covers an emerging topic of wide interest: Crowdsourcing. Specifically, we cover areas of crowdsourcing related to managing structured and unstructured data in a web-related content. Many researchers and practitioners today see the great opportunity that becomes available through easily-available crowdsourcing platforms. However, most newcomers face the same questions: How can we manage the (noisy) crowds to generate high quality output? How to estimate the quality of the contributors? How can we best structure the tasks? How can we get results in small amounts of time and minimizing the necessary resources? How to setup the incentives? How should such crowdsourcing markets be setup?Their presented material will cover topics from a variety of fields, including computer science, statistics, economics, and psychology. Furthermore, the material will include real-life examples and case studies from years of experience in running and managing crowdsourcing applications in business settings.The tutorial presenters have an extensive academic and systems building experience and will provide the audience with data sets that can be used for hands-on tasks.Keywords crowdsourcing mechanical turk workflow control quality assurance incentives reputation market design human computation
In this paper we describe Rabj 1 , an engine designed to simplify collecting human input. We have used Rabj to collect over 2.3 million human judgments to augment data mining, data entry, and curation tasks at Freebase over the course of a year. We illustrate several successful applications that have used Rabj to collect human judgment. We describe how the architecture and design decisions of Rabj are affected by the constraints of content agnosticity, data freshness, latency and visibility. We present work aimed at increasing the yield and reliability of human computation efforts. Finally, we discuss empirical observations and lessons learned in the course of a year of operating the service.
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