research-article

Microtask Detection

Authors:

James Woffinden-Luey,

Mark EncarnacióN,

Sujay Kumar JauharAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 39, Issue 2

Article No.: 17, Pages 1 - 29

https://doi.org/10.1145/3432290

Published: 08 January 2021 Publication History

Abstract

Information systems, such as task management applications and digital assistants, can help people keep track of tasks of different types and different time durations, ranging from a few minutes to days or weeks. Helping people better manage their tasks and their time are core capabilities of assistive technologies, situated within a broader context of supporting more effective information access and use. Throughout the course of a day, there are typically many short time periods of downtime (e.g., five minutes or less) available to individuals. Microtasks are simple tasks that can be tackled in such short amounts of time. Identifying microtasks in task lists could help people utilize these periods of low activity to make progress on their task backlog. We define actionable tasks as self-contained tasks that need to be completed or acted on. However, not all to-do tasks are actionable. Many task lists are collections of miscellaneous items that can be completed at any time (e.g., books to read, movies to watch), notes (e.g., names, addresses), or the individual items are constituents in a list that is itself a task (e.g., a grocery list). In this article, we introduce the novel challenge of microtask detection, and we present machine-learned models for automatically determining which tasks are actionable and which of these actionable tasks are microtasks. Experiments show that our models can accurately identify actionable tasks, accurately detect actionable microtasks, and that we can combine these models to generate a solution that scales microtask detection to all tasks. We discuss our findings in detail, along with their limitations. These findings have implications for the design of systems to help people make the most of their time.

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Cited By

Murdock VLee CHersh W(2024)Designing for the Future of Information Access with Generative Information RetrievalInformation Access in the Era of Generative AI10.1007/978-3-031-73147-1_9(223-248)Online publication date: 12-Sep-2024
https://doi.org/10.1007/978-3-031-73147-1_9
Wang YPapangelis KLykourentzou ISaker MChamberlain AKhan VLiang HYue Y(2023)Tasks of a Different Color: How Crowdsourcing Practices Differ per Complex Task Type and Why This MattersProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581418(1-15)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581418
Khaokaew YHolcombe-James IRahaman MLiono JTrippas JSpina DBailey PBelkin NBennett PRen YSanderson MScholer FWhite RSalim F(2022)Imagining future digital assistants at work: A study of task management needsInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2022.102905168(102905)Online publication date: Dec-2022
https://doi.org/10.1016/j.ijhcs.2022.102905
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Index Terms

Microtask Detection
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Intelligent agents
2. Information systems
  1. Information retrieval
    1. Users and interactive retrieval
      1. Task models
  2. Information systems applications

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 39, Issue 2

April 2021

391 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3444752

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 08 January 2021

Accepted: 01 October 2020

Revised: 01 August 2020

Received: 01 May 2020

Published in TOIS Volume 39, Issue 2

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Cited By

Murdock VLee CHersh W(2024)Designing for the Future of Information Access with Generative Information RetrievalInformation Access in the Era of Generative AI10.1007/978-3-031-73147-1_9(223-248)Online publication date: 12-Sep-2024
https://doi.org/10.1007/978-3-031-73147-1_9
Wang YPapangelis KLykourentzou ISaker MChamberlain AKhan VLiang HYue Y(2023)Tasks of a Different Color: How Crowdsourcing Practices Differ per Complex Task Type and Why This MattersProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581418(1-15)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581418
Khaokaew YHolcombe-James IRahaman MLiono JTrippas JSpina DBailey PBelkin NBennett PRen YSanderson MScholer FWhite RSalim F(2022)Imagining future digital assistants at work: A study of task management needsInternational Journal of Human-Computer Studies10.1016/j.ijhcs.2022.102905168(102905)Online publication date: Dec-2022
https://doi.org/10.1016/j.ijhcs.2022.102905
Shah CWhite R(2021)Task Intelligence for Search and RecommendationSynthesis Lectures on Information Concepts, Retrieval, and Services10.2200/S01103ED1V01Y202105ICR07413:3(1-160)Online publication date: 9-Jun-2021
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Zhang CKiseleva JJauhar SWhite R(2021)Grounded Task Prioritization with Context-Aware Sequential RankingACM Transactions on Information Systems10.1145/348686140:4(1-28)Online publication date: 8-Dec-2021
https://dl.acm.org/doi/10.1145/3486861

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