MicroLapse: Measuring workers' leniency to prediction errors of microtasks' working times

Susumu Saito, Teppei Nakano, Tetsunori Kobayashi, Jefrey P. Bigham

研究成果: Conference contribution

抄録

Working time estimation is known to be helpful for allowing crowd workers to select lucrative microtasks. We previously proposed a machine learning method for estimating the working times of microtasks, but a practical evaluation was not possible because it was unclear what errors would be problematic for workers across diferent scales of microtask working times. In this study, we formulate MicroLapse, a function that expresses a maximal error in working time prediction that workers can accept for a given working time length. We collected 60, 760 survey answers from 660 Amazon Mechanical Turk workers to formulate MicroLapse. Our evaluation of our previous method based on MicroLapse demonstrated that our working time prediction method was fairly successful for shorter microtasks, which could not have been concluded in our previous paper.

本文言語English
ホスト出版物のタイトルCSCW 2019 Companion - Conference Companion Publication of the 2019 Computer Supported Cooperative Work and Social Computing
出版社Association for Computing Machinery
ページ352-356
ページ数5
ISBN(電子版)9781450366922
DOI
出版ステータスPublished - 2019 11 9
イベント22nd ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW 2019 - Austin, United States
継続期間: 2019 11 92019 11 13

出版物シリーズ

名前Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW

Conference

Conference22nd ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW 2019
国/地域United States
CityAustin
Period19/11/919/11/13

ASJC Scopus subject areas

  • ソフトウェア
  • コンピュータ ネットワークおよび通信
  • 人間とコンピュータの相互作用

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