QuaLLM: An LLM-based Framework to Extract Quantitative Insights from Online Forums

TL;DR

QuaLLM is a new LLM-based framework that efficiently extracts quantitative insights from online forum data, reducing human effort and enabling large-scale analysis of community concerns.

Contribution

It introduces a novel prompting and evaluation methodology for LLMs to analyze online discussions, demonstrated on the largest Reddit rideshare worker study to date.

Findings

Identified significant worker concerns about AI and algorithms.

Analyzed over one million comments from Reddit communities.

Set a new standard for AI-assisted quantitative forum analysis.

Abstract

Online discussion forums provide crucial data to understand the concerns of a wide range of real-world communities. However, the typical qualitative and quantitative methodologies used to analyze those data, such as thematic analysis and topic modeling, are infeasible to scale or require significant human effort to translate outputs to human readable forms. This study introduces QuaLLM, a novel LLM-based framework to analyze and extract quantitative insights from text data on online forums. The framework consists of a novel prompting and human evaluation methodology. We applied this framework to analyze over one million comments from two of Reddit's rideshare worker communities, marking the largest study of its type. We uncover significant worker concerns regarding AI and algorithmic platform decisions, responding to regulatory calls about worker insights. In short, our work sets a new precedent for AI-assisted quantitative data analysis to surface concerns from online forums.