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Advancing content synthesis in macro-task crowdsourcing facilitation leveraging natural language processing

  • Macro-task crowdsourcing presents a promising approach to address wicked problems like climate change by leveraging the collective efforts of a diverse crowd. Such macro-task crowdsourcing requires facilitation. However, in the facilitation process, traditionally aggregating and synthesizing text contributions from the crowd is labor-intensive, demanding expertise and time from facilitators. Recent advancements in large language models (LLMs) have demonstrated human-level performance in natural language processing. This paper proposes an abstract design for an information system, developed through four iterations of a prototype, to support the synthesis process of contributions using LLM-based natural language processing. The prototype demonstrated promising results, enhancing efficiency and effectiveness in synthesis activities for macro-task crowdsourcing facilitation. By streamlining the synthesis process, the proposed system significantly reduces the effort to synthesize content,Macro-task crowdsourcing presents a promising approach to address wicked problems like climate change by leveraging the collective efforts of a diverse crowd. Such macro-task crowdsourcing requires facilitation. However, in the facilitation process, traditionally aggregating and synthesizing text contributions from the crowd is labor-intensive, demanding expertise and time from facilitators. Recent advancements in large language models (LLMs) have demonstrated human-level performance in natural language processing. This paper proposes an abstract design for an information system, developed through four iterations of a prototype, to support the synthesis process of contributions using LLM-based natural language processing. The prototype demonstrated promising results, enhancing efficiency and effectiveness in synthesis activities for macro-task crowdsourcing facilitation. By streamlining the synthesis process, the proposed system significantly reduces the effort to synthesize content, allowing for stronger integration of synthesized content into the discussions to reach consensus, ideally leading to more meaningful outcomes.show moreshow less

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Metadaten
Author:Henner GimpelORCiDGND, Robert Laubacher, Oliver Meindl, Moritz Wöhl, Luca Dombetzki
URN:urn:nbn:de:bvb:384-opus4-1147767
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/114776
ISSN:0926-2644OPAC
ISSN:1572-9907OPAC
Parent Title (English):Group Decision and Negotiation
Publisher:Springer Science and Business Media LLC
Type:Article
Language:English
Year of first Publication:2024
Publishing Institution:Universität Augsburg
Release Date:2024/08/26
Volume:33
Issue:5
First Page:1301
Last Page:1322
DOI:https://doi.org/10.1007/s10726-024-09894-w
Institutes:Mathematisch-Naturwissenschaftlich-Technische Fakultät
Wirtschaftswissenschaftliche Fakultät
Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Materials Resource Management
Mathematisch-Naturwissenschaftlich-Technische Fakultät / Institut für Materials Resource Management / Professur für Wirtschaftsingenieurwesen
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
Licence (German):License LogoCC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)