Mining Reliable Information from Passively and Actively Crowdsourced Data

author: Jiawei Han, Department of Computer Science, University of Illinois at Urbana-Champaign
author: Wei Fan, Baidu, Inc.
author: Bo Zhao, LinkedIn Corporation
author: Qi Li, Department of Computer Science and Engineering, University at Buffalo
author: Jing Gao, Department of Computer Science and Engineering, University at Buffalo
published: Sept. 9, 2016,   recorded: August 2016,   views: 2278

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Recent years have witnessed an astonishing growth of crowd-contributed data, which has become a powerful information source that covers almost every aspect of our lives. This big treasure trove of information has fundamentally changed the ways in which we learn about our world. Crowdsourcing has attracted considerable attentions with various approaches developed to utilize these enormous crowdsourced data from different perspectives. From the data collection perspective, crowdsourced data can be divided into two types: "passively" crowdsourced data and "actively" crowdsourced data; from task perspective, crowdsourcing research includes information aggregation, budget allocation, worker incentive mechanism, etc. To answer the need of a systematic introduction of the field and comparison of the techniques, we will present an organized picture on crowdsourcing methods in this tutorial. The covered topics will be interested for both advanced researchers and beginners in this field.

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