# Applications of Large Language Models in Public Opinion Research: From Silicon-Based Sampling to a Practical Guide

> This is the companion code repository for a new book by Cambridge University Press, which systematically introduces how to use large language models for public opinion research, covering the complete workflow including API calls, prompt engineering, synthetic data generation and validation.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-26T18:39:19.000Z
- 最近活动: 2026-05-26T18:53:23.504Z
- 热度: 150.8
- 关键词: 大语言模型, 民意研究, 硅基采样, 社会科学, 提示工程, 合成数据, R语言, 调查研究
- 页面链接: https://www.zingnex.cn/en/forum/thread/geo-github-bshor-llms-for-public-opinion-element
- Canonical: https://www.zingnex.cn/forum/thread/geo-github-bshor-llms-for-public-opinion-element
- Markdown 来源: floors_fallback

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## Introduction: A Practical Guide to LLM Applications in Public Opinion Research

This article introduces the companion GitHub code repository for the monograph *Large Language Models for Public Opinion Research: A Practical Guide* published by Cambridge University Press, maintained by authors such as Ryan Kennedy. It systematically explains how to use large language models (LLMs) for public opinion research, covering the complete workflow including API interaction, prompt engineering, and silicon-based sampling (synthetic data generation and validation). The code is written in R language, and it also discusses methodological controversies and diverse application scenarios.

## Background: New Tools and Challenges in Public Opinion Research

Traditional public opinion surveys rely on expensive telephone interviews or online questionnaires, with high sample acquisition costs and long cycles. The rise of LLMs has spawned a new paradigm of "silicon-based sampling"—allowing AI to simulate human respondents to generate synthetic data, promising low-cost and rapid generation of large-scale data, but also sparking profound controversies about validity and representativeness.

## Core Methods: From API Interaction to Silicon-Based Sampling Practice

The project consists of two core chapters: Chapter 1 introduces the basics of the Transformer architecture, OpenAI API calls (simplified by the ellmer package), the CREATE prompt framework (Context/Role/Examples/Audience/Tone/Extras), and control of output parameters (such as temperature); Chapter 2 explains the silicon-based sampling process: demographic feature extraction → prompt template construction → GPT-5-mini generation of simulated responses → structured storage of results.

## Validation Strategy: Validity Assessment of Synthetic Data

Using a subset of the 2021 Cooperative Congressional Election Study (CCES) as benchmark data, multi-dimensional validation is adopted: distribution comparison (differences in key variable distributions), cross-tabulation analysis (variable association patterns), statistical inference (model coefficient comparison); meanwhile, an Ollama local model option is provided to address API cost and privacy concerns.

## Methodological Controversies: Potential and Limitations of AI-Generated Data

Supporting views: high cost-effectiveness, precise experimental control, privacy protection; skeptical voices: training data bias, questionable authenticity of responses, external validity to be verified. The authors do not avoid controversies; they demonstrate methods for evaluating the quality of synthetic data through strict validation processes and maintain a cautious attitude.

## Application Scenarios: Value for Different Groups

Social science researchers: learn AI-assisted survey experiments; data scientists: master best practices in prompt engineering; policy analysts: obtain public opinions at low cost; contributors to methodological literature: promote the establishment of field norms.

## Summary and Outlook: The Future of LLMs in Public Opinion Research

This project is a milestone in the application of LLMs to social sciences, providing runnable code and a methodological framework (responsibly integrating AI tools, balancing innovation and rigor); as LLM capabilities improve, silicon-based sampling may become a routine tool in public opinion research, and this project provides the technical foundation and ethical guidelines for this transition.