Using Large Language Models in Cluster Analysis in the Social Sciences

The law, regulation, and policy of and for the digital economy can be viewed through different lenses. These include the formal approaches used by lawyers and academics through analysis by news businesses to content shared in video or audio form. Understanding the commonality and differences between the view through each of the lenses requires coordinated data sources. The International Digital Policy Observatory (IDPO) was created to develop a dataset across a variety of sources. This article demonstrates a novel methodological approach that uses data from the IDPO to analyze the interaction between different data sources. It does this using artificial intelligence regulation as an example and combines Gaussian Mixture Model (GMM) clustering techniques with Large Language Models (LLMs) for interpretable cluster naming to identify themes flowing from the data. It sets out the thematic outcomes in the context of each of the data source types to illustrate the method’s utility. This article’s primary contribution is methodological, presenting a scalable and interpretable workflow for analyzing large, multi-source text datasets in social science research. The clustering approach used is likely to be helpful in the analysis of text metadata in other large datasets.