In building our analysis pipeline, we had to consider several issues. First, <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic">LLMs are rarely trained to work directly on video input</hi>, so we decided to operate on individual frames rather than on the video as a whole. In particular, Chen (2023) suggests this approach for analysing video with the GPT-4 model family. Second, <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic">closed-source LLMs often outperform open-source LLMs</hi>. While many promising multimodal networks are available online, the GPT-4 model family still outperforms them on various metrics (Fu et al., 2023; Yin et al., 2023; Fu et al., 2024), but it uses a subscription model and has to be paid for. Therefore, we decided to compare the performance of <hi xmlns="http://www.tei-c.org/ns/1.0" rend="color(#0e101a)">GPT-4o mini</hi> with that of an open-source network. Third, <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic">automated image analysis still has substantial hardware requirements</hi>, most of which are not feasible to run outside of HPC clusters. After some experimentation, we chose LLaVA-v1.5 with 13 billion parameters and 4-bit encoding, as this network offers reasonable performance during inference and can be run on a single A100 GPU. Fourth, <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic">LLMs are notoriously black boxes</hi>, so we used them as little as possible. While LLMs can be used to create full-text descriptions of videos (e.g. Chen, 2023), we limited the automated work to keyword annotation. This approach also allowed us to compare and process the annotations more easily.