Kurator: Using the crowd to help families with personal curation tasks

David Merritt, Jasmine Jones, Mark S. Ackerman, Walter S. Lasecki

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

8 Scopus citations

Abstract

People capture photos, audio recordings, video, and more on a daily basis, but organizing all these digital artifacts quickly becomes a daunting task. Automated solutions struggle to help us manage this data because they cannot understand its meaning. In this paper, we introduce Kurator, a hybrid intelligence system leveraging mixed-expertise crowds to help families curate their personal digital content. Kurator produces a refined set of content via a combination of automated systems able to scale to large data sets and human crowds able to understand the data. Our results with 5 families show that Kurator can reduce the amount of effort needed to find meaningful memories within a large collection. This work also suggests that crowdsourcing can be used effectively even in domains where personal preference is key to accurately solving the task.

Original languageEnglish (US)
Title of host publicationCSCW 2017 - Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing
PublisherAssociation for Computing Machinery
Pages1835-1849
Number of pages15
ISBN (Electronic)9781450343350
DOIs
StatePublished - Feb 25 2017
Event2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2017 - Portland, United States
Duration: Feb 25 2017Mar 1 2017

Other

Other2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 2017
CountryUnited States
CityPortland
Period2/25/173/1/17

Keywords

  • Crowdsourcing
  • Digital audio
  • Digital curation
  • Hybrid intelligence
  • Mixed-expertise
  • Personal curation

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