  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.
