Svensson, JonasBouma, GerlofDannélls, DanaKokkinakis, DimitriosVolodina, Elena2025-11-102025-11-102025-119789908536125https://hdl.handle.net/10062/117346https://doi.org/10.58009/aere-perennius0176The chapter suggests and provides an example of a Large Language Model (LLM)-augmented method for gaining a quick overview of large sets of YouTube videos using metadata collected through the YouTube API. The case chosen is the Swedish Salafist YouTube channel islam.nu that houses 1 680 videos. An LLM (GPT-4o mini) is given a prompt to guess the content of videos based on information given in their titles and descriptions. These guesses are then used in an LLM-augmented topic modeling process utilizing the Python library BERTopic and the HUMINFRA resource, the Swedish Royal Library’s sentencetransformers model “sentence-bert-swedish-cased”. The videos thus placed under topics are then again subjected to processing by an LLM, to produce easyto-read representations of the topics. This method provides a convenient way to quickly understand the content of YouTube video sets and can serve as a first step in a purposive sampling procedure.enAttribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/Article