Maximizing benefits from crowdsourced data

Geoffrey Barbier (), Reza Zafarani (), Huiji Gao (), Gabriel Fung () and Huan Liu ()
Additional contact information
Geoffrey Barbier: Air Force Research Laboratory
Reza Zafarani: Arizona State University
Huiji Gao: Arizona State University
Gabriel Fung: IGNGAB Lab
Huan Liu: Arizona State University

Computational and Mathematical Organization Theory, 2012, vol. 18, issue 3, No 2, 257-279

Abstract: 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.

Keywords: Crowdsourcing; Event maps; Community maps; Crisis maps; Social media; Data mining; Machine learning; Humanitarian Aid and Disaster Relief (HADR) (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s10588-012-9121-2

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