ORCID

https://orcid.org/0000-0001-9953-8684

Keywords

Large Language Models, Argument Mapping, Semantic Structure Extraction, Public Opinion Analysis, Decision Support Systems, Computational Democracy

Abstract

The growing volume of opinion data presents a significant challenge for policymakers striving to distill public sentiment into actionable decisions. This study aims to explore the capability of large language models (LLMs) to synthesize public opinion data into coherent policy recommendations. We specifically leverage Mistral 7B and Mixtral 8x7B models for text generation and have developed an architecture to process vast amounts of unstructured information, integrate diverse viewpoints, and extract actionable insights aligned with public opinion. Using a retrospective data analysis of the Polis platform debates published by the Computational Democracy Project, this study examines multiple datasets that span local and national issues with 1600 statements posted and voted upon by over 3400 participants. Through content moderation, topic modeling, semantic structure extraction, insight generation, and argument mapping, we dissect and interpret the comments, leveraging voting data and LLMs for both quantitative and qualitative insights. A key contribution of this thesis is demonstrating how LLM reasoning techniques can enhance content moderation. Our content moderation approach shows performance improvements using comment deconstruction in multi-class classification, underscoring the trade-offs between moderation strategies and emphasizing a balance between precision and cautious moderation. Using comment clustering, we establish a hierarchy of semantically linked topics, facilitating an understanding of thematic structures and the generation of actionable insights. The generated argument maps visually represent the relationships between topics and insights, and highlight popular opinions. Future work will leverage advanced semantic extraction and reasoning techniques to enhance insight generation further. We also plan to generalize our techniques to other major discussion platforms, including Kialo. Our work contributes to the understanding of using LLMs for policymaking and offers a novel approach to structuring complex debates and translating public opinion into actionable policy insights.

Completion Date

2024

Semester

Spring

Committee Chair

Sukthankar, Gita

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Department

Department of Computer Science

Degree Program

Computer Science

Format

application/pdf

Identifier

DP0028373

URL

https://purls.library.ucf.edu/go/DP0028373

Language

English

Rights

In copyright

Release Date

May 2024

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

Campus Location

Orlando (Main) Campus

Accessibility Status

Meets minimum standards for ETDs/HUTs

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