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Crowdsourcing Team Formation With Worker-Centered Modeling
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 r...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9184727/ https://www.ncbi.nlm.nih.gov/pubmed/35692938 http://dx.doi.org/10.3389/frai.2022.818562 |
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author | Vinella, Federica Lucia Hu, Jiayuan Lykourentzou, Ioanna Masthoff, Judith |
author_facet | Vinella, Federica Lucia Hu, Jiayuan Lykourentzou, Ioanna Masthoff, Judith |
author_sort | Vinella, Federica Lucia |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9184727 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91847272022-06-11 Crowdsourcing Team Formation With Worker-Centered Modeling Vinella, Federica Lucia Hu, Jiayuan Lykourentzou, Ioanna Masthoff, Judith Front Artif Intell Artificial Intelligence 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. Frontiers Media S.A. 2022-05-27 /pmc/articles/PMC9184727/ /pubmed/35692938 http://dx.doi.org/10.3389/frai.2022.818562 Text en Copyright © 2022 Vinella, Hu, Lykourentzou and Masthoff. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Artificial Intelligence Vinella, Federica Lucia Hu, Jiayuan Lykourentzou, Ioanna Masthoff, Judith Crowdsourcing Team Formation With Worker-Centered Modeling |
title | Crowdsourcing Team Formation With Worker-Centered Modeling |
title_full | Crowdsourcing Team Formation With Worker-Centered Modeling |
title_fullStr | Crowdsourcing Team Formation With Worker-Centered Modeling |
title_full_unstemmed | Crowdsourcing Team Formation With Worker-Centered Modeling |
title_short | Crowdsourcing Team Formation With Worker-Centered Modeling |
title_sort | crowdsourcing team formation with worker-centered modeling |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9184727/ https://www.ncbi.nlm.nih.gov/pubmed/35692938 http://dx.doi.org/10.3389/frai.2022.818562 |
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