Unlocking 18th- and 19th-century Playbills with AI: An Experiment in Qualitative Data Categorization

Abstract

Recent advances in large language models (LLMs) have generated excitement from scholars in the humanities about the ways our research might be transformed with their successful integration, particularly for tasks that require multiclass classification or categorization. This paper offers an interdisciplinary exploration by researchers in the fields of drama and computer science to use generative AI techniques to categorize, tag, and analyze qualitative information from eighteenth- and nineteenth-century British playbills. Our goal was to replicate the process of manually tagging playbills via categorization in a manner that preserves playbills’ intricate nested data structures. Our results indicate that there is promising work to be done on humanities data where there is already an underlying classification structure in place; at the same time, our attempts revealed unexpected layers of complexity and ambiguity in this dataset, particularly around genre categorization and nested performance data. Ultimately, we wish to highlight not just LLMs’ capacity to categorize historical data at scale but also to shed new light on that data’s existing intricacies.