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Maximizing benefits from crowdsourced data

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.

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Notes

  1. http://www.threadless.com/.

  2. http://www.istockphoto.com.

  3. http://www2.innocentive.com/.

  4. http://www.noula.ht/.

  5. http://www.wikipedia.org/.

  6. http://www.openstreetmap.org/.

  7. http://www.ushahidi.com/.

  8. http://sahanafoundation.org/.

  9. http://twitter.com/.

  10. http://epic.cs.colorado.edu/.

  11. http://twitter.pbworks.com/Hashtags—a label for Tweets prefixed with the # character.

  12. http://www.wefeelfine.org/.

  13. http://twittersentiment.appspot.com/.

  14. http://haiti.ushahidi.com/download/.

  15. https://www.mturk.com/mturk/welcome.

  16. http://www.icrc.org/.

  17. http://www.doctorswithoutborders.org/.

  18. http://www.ushahidi.com/.

  19. http://legacy.ushahidi.com/.

  20. http://haiti.ushahidi.com/.

  21. http://swift.ushahidi.com/.

  22. http://haiti.ushahidi.com/reports/view/4008.

  23. http://haiti.ushahidi.com/reports/view/3971.

  24. http://code.google.com/apis/language/.

  25. Results from http://translate.google.com using “Detect Language” feature.

  26. Results from http://cogcomp.cs.illinois.edu/demo/ner/results.php online named entity recognition demonstration.

  27. http://www.esi911.com/esi/.

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Acknowledgements

The authors wish to acknowledge the members of the Arizona State University, Data Mining and Machine Learning laboratory for their motivating influence and thought-inspiring comments and questions with reference to this topic. This work, in particular, the content of Sect. 5, was inspired by and based on an ongoing project “ASU Coordination Tracker (ACT) for Disaster Relief”. This work was funded, in part, by the Office of Naval Research (ONR), the Air Force Office of Scientific Research (AFOSR), and the OSD-T&E (Office of Secretary Defense-Test and Evaluation), Defense-Wide/PE0601120D8Z National Defense Education Program (NDEP)/BA-1, Basic Research; SMART Program Office, www.asee.org/fellowships/smart, Grant Number N00244-09-1-0081. This work is approved for public release, case number 88ABW-2012-1644.

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Correspondence to Geoffrey Barbier.

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Barbier, G., Zafarani, R., Gao, H. et al. Maximizing benefits from crowdsourced data. Comput Math Organ Theory 18, 257–279 (2012). https://doi.org/10.1007/s10588-012-9121-2

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  • DOI: https://doi.org/10.1007/s10588-012-9121-2

Keywords

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