@inproceedings{nagaraj-rao-etal-2025-quallm,
    title = "{Q}ua{LLM}: An {LLM}-based Framework to Extract Quantitative Insights from Online Forums",
    author = "Nagaraj Rao, Varun  and
      Agarwal, Eesha  and
      Dalal, Samantha  and
      Calacci, Dana  and
      Monroy-Hern{\'a}ndez, Andr{\'e}s",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.findings-naacl.74/",
    pages = "1355--1369",
    ISBN = "979-8-89176-195-7",
    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."
}