Practical Guide to Large Language Models in Public Opinion Research: Methods, Code, and Datasets

This article introduces the open-source code repository accompanying the book Large Language Models for Public Opinion Research: A Practical Guide published by Cambridge University Press, covering core methodologies, implementation code, and sample datasets for using LLMs in public opinion research. The project is maintained by bshor, hosted on GitHub, with the original link: https://github.com/bshor/llms-for-public-opinion-element, and the release/update time is 2026-05-29T23:15:11Z.

Traditional public opinion research relies on manual coding and statistical analysis, which faces challenges in data scale when dealing with massive digital content such as social media posts and online comments. The emergence of LLMs provides new possibilities for processing unstructured text. The book and its accompanying code repository, written by Kennedy, Shor, and Austin, aim to provide social science researchers with a systematic methodological framework to guide the responsible and effective application of LLMs in public opinion research.

The methodology emphasizes three key principles: 1. Prompt Engineering and Task Design: Construct structured prompts to transform research questions into tasks executable by LLMs, considering model limitations to avoid bias; 2. Validation and Calibration Strategies: Compare with manual coding, cross-validation, multi-model consistency checks, and quantify output uncertainty; 3. Bias Detection and Mitigation: Use tools to identify model biases, and reduce their impact on results through prompt adjustments and post-processing.

The code repository includes: 1. Data Preprocessing Module: Clean social media text, process multilingual content, standardize formats, etc.; 2. LLM Interaction Interface: Support mainstream LLM APIs (e.g., OpenAI GPT, Anthropic Claude), abstract differences for easy switching, and include rate limiting, error retry, and cost monitoring; 3. Analysis and Visualization Tools: Topic modeling, sentiment analysis, stance detection, trend visualization, etc., to help extract insights and present results according to academic standards.

The sample datasets demonstrate multiple application scenarios: 1. Social Media Opinion Tracking: Analyze Twitter/X discussions to identify the evolution trajectory of issues and key turning points; 2. Policy Feedback Analysis: Analyze public responses to new policies, including sentiment classification and argument extraction; 3. Cross-Cultural Opinion Comparison: Use the multilingual capabilities of LLMs to compare public views on the same issue across different cultural backgrounds.

The project reminds researchers: LLMs are auxiliary tools rather than substitutes; key judgments require human participation; transparency is crucial—detailed records of model selection, prompt design, and validation processes are needed; privacy protection is a bottom line—platform policies and data protection regulations must be followed; result interpretation needs to be cautious to avoid over-inferring the real public opinions behind LLM outputs.

This open-source project provides social science researchers with a valuable starting point for applying AI technology to traditional fields, establishing a framework that can be updated with technological progress. As LLM technology develops, the methodology of public opinion research will continue to evolve, and this project lays the foundation for future research.