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.

We investigate the use of Aspect-Based Sentiment Analysis (ABSA) to analyze polarization in online discourse. For the analysis, we use a corpus of over 3 million user comments and replies from four state-funded media channels from YouTube Shorts in the context of the 2023 Israel–Hamas war. We first annotate a subsample of approx. 5 000 comments for positive, negative, and neutral sentiment towards a list of topic related aspects. After training an ABSA model (Yang et al., 2023) on the corpus, we evaluate its performance on this task intrinsically, before evaluating the usability of the automatic analysis of the whole corpus for analyzing polarization. Our results show that the ABSA model achieves an F1 score of 77.9. The longitudinal and outlet analyses corroborate known trends and offer subject experts more fine-grained information about the use of domain-specific language in user-generated content.