Abstract
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.
Abstract (translated)
YouTube Shorts已成为该平台上新闻消费的核心形式，然而关于地缘政治事件在此类短视频中的呈现方式研究仍较为匮乏。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）及语义场景分类的多模态分析流程。该流程首先经过可行性验证，随后被应用于分析国有媒体对以色列-哈马斯战争的短视频报道。通过2300余条冲突相关Shorts及超过9.4万帧视觉画面，我们系统考察了主要国际广播机构的战争报道模式。研究发现：各媒体在转录文本中对特定方面的情感表达存在差异且随时间变化，而场景类型分类则反映了与现实事件一致的视觉线索。值得注意的是，针对特定领域优化的较小模型在情感分析任务中表现优于大型Transformer模型乃至大语言模型，这凸显了资源高效型方法在人文研究中的价值。该流程可为TikTok、Instagram等其他短视频平台提供参考模板，并展示了如何通过多模态方法结合质性解读，来刻画算法驱动视频环境中的情感模式与视觉线索。
URL
https://arxiv.org/abs/2604.00994
