Advancing Content Synthesis in Macro-Task Crowdsourcing Facilitation Leveraging Natural Language Processing

Title data

Gimpel, Henner ; Laubacher, Robert ; Meindl, Oliver ; Wöhl, Moritz ; Dombetzki, Luca:
Advancing Content Synthesis in Macro-Task Crowdsourcing Facilitation Leveraging Natural Language Processing.
In: Group Decision and Negotiation. Vol. 33 (2024) . - pp. 1301-1322.
ISSN 1572-9907
DOI: https://doi.org/10.1007/s10726-024-09894-w

Official URL:

Abstract in another language

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.

Further data

Item Type:	Article in a journal
Refereed:	Yes
Keywords:	Action design research; Facilitation; Large language model; Macrotask crowdsourcing; Natural language processing; Synthesis
Institutions of the University:	Faculties > Faculty of Law, Business and Economics > Department of Business Administration Research Institutions Research Institutions > Affiliated Institutes Research Institutions > Affiliated Institutes > Branch Business and Information Systems Engineering of Fraunhofer FIT Research Institutions > Affiliated Institutes > FIM Research Center for Information Management Faculties Faculties > Faculty of Law, Business and Economics
Result of work at the UBT:	No
DDC Subjects:	000 Computer Science, information, general works > 004 Computer science 300 Social sciences > 330 Economics
Date Deposited:	30 Sep 2024 09:31
Last Modified:	03 Dec 2024 12:43
URI:	https://eref.uni-bayreuth.de/id/eprint/90491