mlsanityData to 14 Oct 2026

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

arXiv 2405.05345 ↗First posted 8 May 2024cs.CL cs.HCcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 13 of its 73 references that are in the map. First posted on arXiv 8 May 2024.

Builds on

13 of 73 references.

References · 13

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  3. 2023Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicinecited by 329
  4. 2023Large Language Models are Effective Text Rankers with Pairwise Ranking Promptingcited by 307
  5. 2023Supporting Qualitative Analysis with Large Language Models: Combining Codebook with GPT-3 for Deductive Codingcited by 82
  6. 2023LLM-in-the-loop: Leveraging Large Language Model for Thematic Analysiscited by 70
  7. 2023LLM-Assisted Content Analysis: Using Large Language Models to Support Deductive Codingcited by 64
  8. 2023From Sparse to Dense: GPT-4 Summarization with Chain of Density Promptingcited by 52
  9. 2023Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomiescited by 34
  10. 2024The illusion of artificial inclusioncited by 34
  11. 2024Scalable Qualitative Coding with LLMs: Chain-of-Thought Reasoning Matches Human Performance in Some Hermeneutic Taskscited by 22
  12. 2021No Echo in the Chambers of Political Interactions on Redditcited by 9
1 more
  1. 2023Upvotes? Downvotes? No Votes? Understanding the relationship between reaction mechanisms and political discourse on Redditcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 4.

8 papers.

  1. 2024Examining the Prevalence and Dynamics of AI-Generated Media in Art Subredditscited by 8
  2. 2025LOGOS: LLM-driven End-to-End Grounded Theory Development and Schema Induction for Qualitative Researchcited by 6
  3. 2025Who's Asking? Simulating Role-Based Questions for Conversational AI Evaluationcited by 2
  4. 2025FareShare: A Tool for Labor Organizers to Estimate Lost Wages and Contest Arbitrary AI and Algorithmic Deactivationscited by 0
  5. 2026Bridging Human Interpretation and Machine Representation: A Landscape of Qualitative Data Analysis in the LLM Eracited by 0
  6. 2026Can LLMs Understand the Impact of Trauma? Costs and Benefits of LLMs Coding the Interviews of Firearm Violence Survivorscited by 0
  7. 2026What Do People Actually Want From AI? Mapping Preference Pluralitycited by 0
  8. 2026How AI Coders Discuss, Disagree, and Reach Consensus: Challenges and Opportunities for LLM-Based Qualitative Codingcited by 0