The face of quality in crowdsourcing relevance labels: Demographics, personality and labeling accuracy

Gabriella Kazai*, Jaap Kamps, Natasa Milic-Frayling

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

82 Citations (Scopus)

Abstract

Information retrieval systems require human contributed relevance labels for their training and evaluation. Increasingly such labels are collected under the anonymous, uncontrolled conditions of crowdsourcing, leading to varied output quality. While a range of quality assurance and control techniques have now been developed to reduce noise during or after task completion, little is known about the workers themselves and possible relationships between workers' characteristics and the quality of their work. In this paper, we ask how do the relatively well or poorly-performing crowds, working under specific task conditions, actually look like in terms of worker characteristics, such as demographics or personality traits. Our findings show that the face of a crowd is in fact indicative of the quality of their work.

Original languageEnglish
Title of host publicationCIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Pages2583-2586
Number of pages4
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States
Duration: 29 Oct 20122 Nov 2012

Publication series

NameACM International Conference Proceeding Series

Conference

Conference21st ACM International Conference on Information and Knowledge Management, CIKM 2012
Country/TerritoryUnited States
CityMaui, HI
Period29/10/122/11/12

Keywords

  • crowdsourcing
  • demographics
  • personality traits
  • worker accuracy

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