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Toward Crowdsourcing Data Mining

  • Hsin-Chang YangEmail author
  • Chung-Hong Lee
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Nowadays, crowdsourcing has emerged as a popular and important problem-solving approach. The major difference between crowdsourcing and traditional outsourcing lies on the people which tasks were outsourced. Those people involved in crowdsourcing are generally varied in knowledge, demographic properties, and number. Many applications and services have been developed to solve various types of tasks. However, these applications and services focus on providing platforms for outsourcing to the crowd. Little has been addressed so far on the management and usage of those information produced during the crowdsourcing process. Actually, as an emerging social network application and service, the data and social interactions created during crowdsourcing should carry important and valuable knowledge. This knowledge will develop various techniques for mining messages and information of crowdsourcing process. In this work, we address several approaches to discover useful knowledge from data created for and in crowdsourcing process. We hope the outcome of this research could help discovering usable knowledge from such emerging social network services and bring benefit in constructing crowdsourcing services.

Keywords

Crowdsourcing Text mining Topic detection Association discovery 

References

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    Yang HC, Lee CH, Ke KL (2010) TOSOM: A topic-oriented self-organizing map for text organization. World Acad Sci Eng Technol 41:1100–1104Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Department of Information ManagementNational University of KaohsiungKaohsiungTaiwan
  2. 2.Department of Electrical EngineeringNational Kaohsiung University of Applied SciencesKaohsiungTaiwan

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