Debiasing crowdsourced quantitative characteristics in local businesses and services

Robin Wentao Ouyang, Lance Kaplan, Paul Martin, Alice Toniolo, Mani Srivastava, Timothy J. Norman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

31 Citations (Scopus)

Abstract

Information about quantitative characteristics in local businesses and services, such as the number of people waiting in line in a cafe and the number of available fitness machines in a gym, is important for informed decision, crowd management and event detection. In this paper, we investigate the potential of leveraging crowds as sensors to report such quantitative characteristics and investigate how to recover the true quantity values from noisy crowdsourced information. Through experiments, we find that crowd sensors have both bias and variance in quantity sensing, and task difficulties impact the sensing accuracy. Based on these findings, we propose an unsupervised probabilistic model to jointly assess task difficulties, ability of crowd sensors and true quantity values. Our model differs from existing categorical truth finding models as ours is specifically designed to tackle quantitative truth. In addition to devising an efficient model inference algorithm in a batch mode, we also design an even faster online version for handling streaming data. Experimental results in various scenarios demonstrate the effectiveness of our model.
Original languageEnglish
Title of host publicationProceedings of the 14th International Conference on Information Processing in Sensor Networks
Place of PublicationNew York, NY
PublisherACM
Pages190-201
ISBN (Electronic)9781450334754
DOIs
Publication statusPublished - 13 Apr 2015
Event14th International Conference on Information Processing in Sensor Networks (IPSN '15) - Seattle , United States
Duration: 13 Apr 201516 Apr 2015
Conference number: 14
http://ipsn.acm.org/2015/

Conference

Conference14th International Conference on Information Processing in Sensor Networks (IPSN '15)
Abbreviated titleIPSN
Country/TerritoryUnited States
CitySeattle
Period13/04/1516/04/15
Internet address

Keywords

  • Crowdsourcing
  • Humans as sensors
  • Truth discovery
  • Probabilistic graphical models

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