Moderating Large Scale Online Deliberative Processes with Large Language Models (LLMs): Enhancing Collective Decision-Making.
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3672608.3707925.pdf
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1.39 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Babatunde, Ibukun
Nnanna, Obiabuchi
Klein, Mark
Date Issued
March 31, 2025
Publisher
ACM|The 40th ACM/SIGAPP Symposium on Applied Computing
Citation
Babatunde, Ibukun, Nnanna, Obiabuchi and Klein, Mark. 2025. "Moderating Large Scale Online Deliberative Processes with Large Language Models (LLMs): Enhancing Collective Decision-Making.."
Version
Final published version
Abstract
This study investigates the use of LLMs, specifically ChatGPT-4o, to enhance the moderation of online deliberative processes. Traditionally, decision-making has been controlled by small groups, often excluding the vital insights that crowd intelligence can provide. As global challenges grow more complex, broader and more inclusive participation is essential. While online platforms allow for such large-scale participation, they also face significant issues, including content fragmentation, low signal-to-noise ratios, and inefficient argumentation. Human moderators can address these challenges, but scaling them is prohibitively costly. This research introduces a more scalable solution by leveraging LLMs to automate critical moderation tasks, including unbundling multiple ideas, categorizing them into solutions, metrics, and barriers, and implementing efficient argument mining and classification techniques. Additionally, it evaluates the effectiveness of different prompting styles in optimizing moderation. The findings demonstrate that LLMs can successfully moderate key aspects of large-scale online deliberations, such as unbundling and categorization, improving the structure of discussions and representing a significant step forward in collective decision-making.
Description
SAC ’25, March 31-April 4, 2025, Catania, Italy
MIT Department
Massachusetts Institute of Technology. Center for Collective Intelligence
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1145/3672608.3707925
