Research Article
PIN: Potential Wise Crowd From Million Grassroots
@INPROCEEDINGS{10.4108/eai.7-11-2017.2273447, author={Yao Wu and Tao Huang and Dan Zhao and Hong Chen and Cuiping Li}, title={PIN: Potential Wise Crowd From Million Grassroots}, proceedings={14th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services}, publisher={ACM}, proceedings_a={MOBIQUITOUS}, year={2018}, month={4}, keywords={mobile crowdsourcing distant supervision crowd formation mobile recruitment framework}, doi={10.4108/eai.7-11-2017.2273447} }- Yao Wu
Tao Huang
Dan Zhao
Hong Chen
Cuiping Li
Year: 2018
PIN: Potential Wise Crowd From Million Grassroots
MOBIQUITOUS
ACM
DOI: 10.4108/eai.7-11-2017.2273447
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.