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

LLMs for online text data analysis limits its use and underscores a significant gap in the research landscape. Online discussion forums provide crucial data to understand the Our work addresses this gap through the following contributions: concerns of a wide range of real-world communities. However, the typical qualitative and quantitative methods used to analyze those (i) We introduce QuaLLM, an LLM-based framework consisting data, such as thematic analysis and topic modeling, are infeasible of a novel prompting methodology and evaluation strategy to scale or require significant human effort to translate outputs for the analysis and extraction of quantitative insights from to human readable forms. This study introduces QuaLLM, a novel text data on online forums. LLM-based framework to analyze and extract quantitative insights (ii) We apply our framework to a case study on Reddit's rideshare from text data on online forums. The framework consists of a novel communities, analyzing over one million comments--the prompting methodology and evaluation strategy. We applied this largest study of its kind--to identify worker concerns regarding framework to analyze over one million comments from two Reddit's AI and algorithmic platform decisions, responding to rideshare worker communities, marking the largest study of its regulatory calls [49].