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Article
2024
Advancing content synthesis in macro-task crowdsourcing facilitation leveraging natural language processing
Advancing content synthesis in macro-task crowdsourcing facilitation leveraging natural language processing
Abstract (English)
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.
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Group decision and negotiation, 33 (2024), 5, 1301-1322.
https://doi.org/10.1007/s10726-024-09894-w.
ISSN: 1572-9907
ISSN: 0926-2644
Dordrecht : Springer Netherlands
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Gimpel, H., Laubacher, R., Meindl, O., Wöhl, M., & Dombetzki, L. (2024). Advancing content synthesis in macro-task crowdsourcing facilitation leveraging natural language processing. Group decision and negotiation, 33(5). https://doi.org/10.1007/s10726-024-09894-w
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English
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000 Computer science, information and general works
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Sustainable Development Goals
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@article{Gimpel2024,
doi = {10.1007/s10726-024-09894-w},
author = {Gimpel, Henner and Laubacher, Robert and Meindl, Oliver et al.},
title = {Advancing content synthesis in macro-task crowdsourcing facilitation leveraging natural language processing},
journal = {Group decision and negotiation},
year = {2024},
volume = {33},
number = {5},
pages = {1301--1322},
}