Short-form video is difficult to study at scale because meaning emerges through audiovisual elements, language, and participatory, algorithmic and trend-based platform dynamics. Manual annotation of these layers is laborious at scale and difficult to standardize. We demonstrate how multimodal large language models (LLMs) can help address this bottleneck by annotating a set of 77 theory-driven structural variables derived from narratology, rhetoric, communication, and semiotics. We use this to explore content and estimate engagement with modest but consistent gains over account-size and video-age baselines in a corpus of about 10,000 TikTok videos of brand and organizational accounts from Estonia (covering a substantial share of the small country ecosystem). Human validation shows a reliability gradient: perceptual and communicative variables can be coded fairly reliably, while deeper semiotic and archetypal constructs are more difficult for both humans and machines. This approach of computational operationalization of long-standing interpretive theories can support several aims: exploratory cultural analytics of variation in short-form video culture, predictive modeling of platform dynamics, engagement, and audience feedback; and diagnostics for content creators to support choosing between structural and narrative strategies. Most annotated variables were not associated with platform success, as expected; the value of LLMs in this setting lies in making it feasible to assess large batteries of theoretically motivated variables, so that the subset carrying signal can be identified and translated into creator-facing guidance for a given niche.

翻译：短视频难以大规模研究，因为其意义通过视听元素、语言以及参与性、算法性和趋势性的平台动态共同产生。手动标注这些层面在大规模情况下既费力又难以标准化。我们展示了多模态大语言模型（LLMs）如何通过标注一组源自叙事学、修辞学、传播学和符号学的77个理论驱动结构变量来帮助解决这一瓶颈。我们利用这一点来探索内容并估计参与度，在包含约10,000个来自爱沙尼亚品牌和组织账户的TikTok视频语料库中（覆盖了该小国生态系统的相当大份额），相较于账户规模和视频时长基线，获得了虽小但一致的提升。人工验证显示出一个可靠性梯度：感知和传播变量可以相当可靠地编码，而更深入的符号学和原型构念对人类和机器都更难。这种对长期存在的解释性理论进行计算操作化的方法可以支持多个目标：对短视频文化变异进行探索性文化分析、对平台动态、参与度和受众反馈进行预测建模，以及为内容创作者提供诊断工具以支持其在结构性和叙事性策略之间做出选择。正如预期，大多数标注变量与平台成功无关；在此情境下，LLMs的价值在于使得评估大量理论驱动变量变得可行，从而可以识别出携带信号的子集，并将其转化为针对特定细分市场的创作者指导。