YouTube Shorts have become central to news consumption on the platform, yet research on how geopolitical events are represented in this format remains limited. To address this gap, we present a multimodal pipeline that combines automatic transcription, aspect-based sentiment analysis (ABSA), and semantic scene classification. The pipeline is first assessed for feasibility and then applied to analyze short-form coverage of the Israel-Hamas war by state-funded outlets. Using over 2,300 conflict-related Shorts and more than 94,000 visual frames, we systematically examine war reporting across major international broadcasters. Our findings reveal that the sentiment expressed in transcripts regarding specific aspects differs across outlets and over time, whereas scene-type classifications reflect visual cues consistent with real-world events. Notably, smaller domain-adapted models outperform large transformers and even LLMs for sentiment analysis, underscoring the value of resource-efficient approaches for humanities research. The pipeline serves as a template for other short-form platforms, such as TikTok and Instagram, and demonstrates how multimodal methods, combined with qualitative interpretation, can characterize sentiment patterns and visual cues in algorithmically driven video environments.

翻译：YouTube Shorts已成为该平台新闻消费的核心形式，然而关于地缘政治事件在此类格式中呈现方式的研究仍十分有限。为填补这一空白，我们提出了一种融合自动转录、基于方面的情感分析（ABSA）与语义场景分类的多模态分析流程。首先对该流程的可行性进行评估，随后将其应用于分析国家资助媒体对以色列-哈马斯战争的短视频报道。基于超过2,300条冲突相关Shorts视频及94,000余帧视觉画面，我们系统考察了主要国际广播公司的战争报道。研究发现，不同媒体机构在特定议题的转录文本情感表达上呈现时间与机构间的差异，而场景类型分类则反映出与现实世界事件相吻合的视觉线索。值得注意的是，小规模领域自适应模型在情感分析任务中超越了大型Transformer模型甚至大语言模型（LLMs），凸显了资源高效型方法在人文学科研究中的价值。该分析流程可作为TikTok、Instagram等其他短视频平台的通用模板，并展示了多模态分析方法结合定性解读如何刻画算法驱动视频环境中的情感模式与视觉线索。