Improving Worker Engagement Through Conversational Microtask Crowdsourcing

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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. Our findings have important implications on workflows and task design with regard to better engaging workers in microtask crowdsourcing marketplaces.
Original languageEnglish
Title of host publicationCHI'20
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)
Number of pages12
ISBN (Print)978-1-4503-6708-0/20/04
Publication statusPublished - 2020
EventCHI 2020: The ACM CHI Conference on Human Factors in Computing Systems - Honolulu, United States
Duration: 25 Apr 202030 Apr 2020


ConferenceCHI 2020: The ACM CHI Conference on Human Factors in Computing Systems
CountryUnited States


  • Microtask crowdsourcing
  • Conversational interface
  • Conversational style
  • User engagement
  • Cognitive task load

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