Big Meaning: Qualitative Analysis on Large Bodies of Data Using AI

Samuel Flanders, Melati Nungsari, Mark Cheong Wing Loong
4/11/2025
cs.CL

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Abstract

This study introduces a framework that leverages AI-generated descriptive codes to indicate a text's fecundity--the density of unique human-generated codes--in thematic analysis. Rather than replacing human interpretation, AI-generated codes guide the selection of texts likely to yield richer qualitative insights. Using a dataset of 2,530 Malaysian news articles on refugee attitudes, we compare AI-selected documents to randomly chosen ones by having three human coders independently derive codes. The results demonstrate that AI-selected texts exhibit approximately twice the fecundity. Our findings support the use of AI-generated codes as an effective proxy for identifying documents with a high potential for meaning-making in thematic analysis.

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Cite this paper

@article{flanders2025meaning,
  title  = {Big Meaning: Qualitative Analysis on Large Bodies of Data Using AI},
  author = {Samuel Flanders and Melati Nungsari and Mark Cheong Wing Loong},
  year   = {2025},
  eprint = {2504.08213},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.08213v1}
}

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