Iterative Topic Taxonomy Induction with LLMs: A Case Study of Electoral Advertising
This research developed an end-to-end framework to automatically create interpretable topic labels for unlabeled political advertising text using unsupervised clustering and iterative large language model prompting. The method successfully induced consistent topic taxonomies without needing pre-defined seeds, proving its scalability for analyzing large political discourse.