Improving Worker Engagement Through Conversational Microtask Crowdsourcing

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

19 Citations (Scopus)
186 Downloads (Pure)

Abstract

The rise in popularity of conversational agents has enabled humans to interact with machines more naturally. Recent work has shown that crowd workers in microtask marketplaces can complete a variety of human intelligence tasks (HITs) using conversational interfaces with similar output quality compared to the traditional Web interfaces. In this paper, we investigate the effectiveness of using conversational interfaces to improve worker engagement in microtask crowdsourcing. We designed a text-based conversational agent that assists workers in task execution, and tested the performance of workers when interacting with agents having different conversational styles. We conducted a rigorous experimental study on Amazon Mechanical Turk with 800 unique workers, to explore whether the output quality, worker engagement and the perceived cognitive load of workers can be affected by the conversational agent and its conversational styles. Our results show that conversational interfaces can be effective in engaging workers, and a suitable conversational style has potential to improve worker engagement.

Original languageEnglish
Title of host publicationCHI 2020 - Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
Subtitle of host publicationProceedings of the 2020 CHI Conference on Human Factors in Computing Systems.
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery (ACM)
Pages1-12
Number of pages12
ISBN (Electronic)9781450367080
ISBN (Print)978-1-4503-6708-0/20/04
DOIs
Publication statusPublished - 2020
EventCHI 2020: The ACM CHI Conference on Human Factors in Computing Systems - Honolulu, United States
Duration: 25 Apr 202030 Apr 2020

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

ConferenceCHI 2020: The ACM CHI Conference on Human Factors in Computing Systems
Country/TerritoryUnited States
CityHonolulu
Period25/04/2030/04/20

Bibliographical note

Accepted author manuscript

Keywords

  • cognitive task load
  • conversational interface
  • conversational style
  • microtask crowdsourcing
  • user engagement

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