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Maximizing benefits from crowdsourced data

Author

Listed:
  • 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)

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.

Suggested Citation

  • Geoffrey Barbier & Reza Zafarani & Huiji Gao & Gabriel Fung & Huan Liu, 2012. "Maximizing benefits from crowdsourced data," Computational and Mathematical Organization Theory, Springer, vol. 18(3), pages 257-279, September.
    • Handle: RePEc:spr:comaot:v:18:y:2012:i:3:d:10.1007_s10588-012-9121-2
    DOI: 10.1007/s10588-012-9121-2
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    References listed on IDEAS

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    1. Bernard J. Jansen & Mimi Zhang & Kate Sobel & Abdur Chowdury, 2009. "Twitter power: Tweets as electronic word of mouth," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(11), pages 2169-2188, November.
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    Cited by:

    1. Mathis Poser & Gerrit C. Küstermann & Navid Tavanapour & Eva A. C. Bittner, 2022. "Design and Evaluation of a Conversational Agent for Facilitating Idea Generation in Organizational Innovation Processes," Information Systems Frontiers, Springer, vol. 24(3), pages 771-796, June.
    2. Bairong Wang & Jun Zhuang, 2017. "Crisis information distribution on Twitter: a content analysis of tweets during Hurricane Sandy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(1), pages 161-181, October.
    3. Igor Melnykov & Volodymyr Melnykov, 2020. "A Note on the Formal Implementation of the K-means Algorithm with Hard Positive and Negative Constraints," Journal of Classification, Springer;The Classification Society, vol. 37(3), pages 789-809, October.
    4. Yihong Yuan & Monica Medel, 2016. "Characterizing International Travel Behavior from Geotagged Photos: A Case Study of Flickr," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-18, May.

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