  Despite the growing interest in leveraging Large Language Models (LLMs) for
content analysis, current studies have primarily focused on text-based content.
In the present work, we explored the potential of LLMs in assisting video
content analysis by conducting a case study that followed a new workflow of
LLM-assisted multimodal content analysis. The workflow encompasses codebook
design, prompt engineering, LLM processing, and human evaluation. We
strategically crafted annotation prompts to get LLM Annotations in structured
form and explanation prompts to generate LLM Explanations for a better
understanding of LLM reasoning and transparency. To test LLM's video annotation
capabilities, we analyzed 203 keyframes extracted from 25 YouTube short videos
about depression. We compared the LLM Annotations with those of two human
coders and found that LLM has higher accuracy in object and activity
Annotations than emotion and genre Annotations. Moreover, we identified the
potential and limitations of LLM's capabilities in annotating videos. Based on
the findings, we explore opportunities and challenges for future research and
improvements to the workflow. We also discuss ethical concerns surrounding
future studies based on LLM-assisted video analysis.
