Crowdsourcing Team Formation With Worker-Centered Modeling

Federica Lucia Vinella, Jiayuan Hu, Ioanna Lykourentzou, Judith Masthoff

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Modern crowdsourcing offers the potential to produce solutions for increasingly complex tasks requiring teamwork and collective labor. However, the vast scale of the crowd makes forming project teams an intractable problem to coordinate manually. To date, most crowdsourcing collaborative platforms rely on algorithms to automate team formation based on worker profiling data and task objectives. As a top-down strategy, algorithmic crowd team formation tends to alienate workers causing poor collaboration, interpersonal clashes, and dissatisfaction. In this paper, we investigate different ways that crowd teams can be formed through three team formation models namely bottom-up, top-down, and hybrid. By simulating an open collaboration scenario such as a hackathon, we observe that the bottom-up model forms the most competitive teams with the highest teamwork quality. Furthermore, we note that bottom-up approaches are particularly suitable for populations with high-risk appetites (most workers being lenient toward exploring new team configurations) and high degrees of homophily (most workers preferring to work with similar teammates). Our study highlights the importance of integrating worker agency in algorithm-mediated team formation systems, especially in collaborative/competitive settings, and bears practical implications for large-scale crowdsourcing platforms.

Original languageEnglish
Article number818562
Pages (from-to)1-22
JournalFrontiers in Artificial Intelligence
Volume5
DOIs
Publication statusPublished - 27 May 2022

Keywords

  • agent based modeling
  • crowdsourcing
  • self-organization
  • social computing
  • team formation

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