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arXiv · 2504.08213

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

Samuel Flanders·Melati Nungsari·Mark Cheong Wing Loong
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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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Samuel Flanders, Melati Nungsari, Mark Cheong Wing Loong. 2025-04-11. Big Meaning: Qualitative Analysis on Large Bodies of Data Using AI. https://arxiv.org/abs/2504.08213

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