QuaLLM: An LLM-based Framework to Extract Quantitative Insights from Online Forums
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916615578189824 |
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| author | Rao, Varun Nagaraj Agarwal, Eesha Dalal, Samantha Calacci, Dan Monroy-Hernández, Andrés |
| author_facet | Rao, Varun Nagaraj Agarwal, Eesha Dalal, Samantha Calacci, Dan Monroy-Hernández, Andrés |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05345 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | QuaLLM: An LLM-based Framework to Extract Quantitative Insights from Online Forums Rao, Varun Nagaraj Agarwal, Eesha Dalal, Samantha Calacci, Dan Monroy-Hernández, Andrés Computation and Language Human-Computer Interaction 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. |
| title | QuaLLM: An LLM-based Framework to Extract Quantitative Insights from Online Forums |
| topic | Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2405.05345 |