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

• URL: http://arxiv.org/abs/2405.05345v1
• Date: Wed, 8 May 2024 18:20:03 GMT
• Title: QuaLLM: An LLM-based Framework to Extract Quantitative Insights from Online Forums
• Authors: Varun Nagaraj Rao, Eesha Agarwal, Samantha Dalal, Dan Calacci, Andrés Monroy-Hernández,
• Abstract summary: This study introduces QuaLLM, a novel framework to analyze and extract quantitative insights from text data on online forums. We applied this framework to analyze over one million comments from two Reddit's rideshare worker communities.
• Score: 10.684484559041284
• License: http://creativecommons.org/licenses/by/4.0/
• 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 methods 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 methodology and evaluation strategy. We applied this framework to analyze over one million comments from two 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.

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