Maximizing benefits from crowdsourced data

Geoffrey Barbier, Reza Zafarani, Huiji Gao, Gabriel Fung, Huan Liu

Research output: Contribution to journalArticle

43 Scopus citations

Abstract

Crowds of people can solve some problems faster than individuals or small groups. A crowd can also rapidly generate data about circumstances affecting the crowd itself. This crowdsourced data can be leveraged to benefit the crowd by providing information or solutions faster than traditional means. However, the crowdsourced data can hardly be used directly to yield usable information. Intelligently analyzing and processing crowdsourced information can help prepare data to maximize the usable information, thus returning the benefit to the crowd. This article highlights challenges and investigates opportunities associated with mining crowdsourced data to yield useful information, as well as details how crowdsource information and technologies can be used for response-coordination when needed, and finally suggests related areas for future research.

Original languageEnglish (US)
Pages (from-to)257-279
Number of pages23
JournalComputational and Mathematical Organization Theory
Volume18
Issue number3
DOIs
StatePublished - Sep 2012
Externally publishedYes

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Keywords

  • Community maps
  • Crisis maps
  • Crowdsourcing
  • Data mining
  • Event maps
  • Humanitarian Aid and Disaster Relief (HADR)
  • Machine learning
  • Social media

ASJC Scopus subject areas

  • Modeling and Simulation
  • Applied Mathematics
  • Computer Science(all)
  • Decision Sciences(all)
  • Computational Mathematics

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