Maximizing benefits from crowdsourced data

被引:0
作者
Geoffrey Barbier
Reza Zafarani
Huiji Gao
Gabriel Fung
Huan Liu
机构
[1] Air Force Research Laboratory,
[2] Arizona State University,undefined
[3] IGNGAB Lab,undefined
来源
Computational and Mathematical Organization Theory | 2012年 / 18卷
关键词
Crowdsourcing; Event maps; Community maps; Crisis maps; Social media; Data mining; Machine learning; Humanitarian Aid and Disaster Relief (HADR);
D O I
暂无
中图分类号
学科分类号
摘要
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.
引用
收藏
页码:257 / 279
页数:22
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