Yao Wu
Hong Chen
ABSTRACT
Crowdsourcing proves a viable approach to solve certain large-scale problems by posting tasks distributively to humans and harnessing their knowledge to get results effectively and efficiently. Unfortunately, crowdsourcing suffers from lack of available participants with domain knowledge or skills. In this paper, we propose potential wise crowd (i.e., a crowd with similarity and diversity in domain knowledge) find from million grassroots in social networks. We design and implement a distant-supervision framework to find potential crowdsourcers from existing social networks. A knowledge graph is used to assess the domain knowledge in terms of similarity and diversity. The wise crowd formation is a NP-hard problem and we propose greedy algorithms to approach it. Experimental results show the performance of our framework and algorithms in aspects of effectiveness and efficiency.
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Index Terms
- PIN: Potential Wise Crowd From Million Grassroots
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