Understanding how media rhetoric shapes audience engagement is crucial in the attention economy. This study examines how moral emotional framing by mainstream news channels on YouTube influences user behavior across Korea and the United States. To capture the platform's multimodal nature, combining thumbnail images and video titles, we develop a multimodal moral emotion classifier by fine tuning a vision language model. The model is trained on human annotated multimodal datasets in both languages and applied to approximately 400,000 videos from major news outlets. We analyze engagement levels including views, likes, and comments, representing increasing degrees of commitment. The results show that other condemning rhetoric expressions of moral outrage that criticize others morally consistently increase all forms of engagement across cultures, with effects ranging from passive viewing to active commenting. These findings suggest that moral outrage is a particularly effective emotional strategy, attracting not only attention but also active participation. We discuss concerns about the potential misuse of other condemning rhetoric, as such practices may deepen polarization by reinforcing in group and out group divisions. To facilitate future research and ensure reproducibility, we publicly release our Korean and English multimodal moral emotion classifiers.

翻译：理解媒体修辞如何塑造受众参与度在注意力经济中至关重要。本研究探讨了YouTube上主流新闻频道的道德情绪框架如何影响韩国和美国用户的参与行为。为捕捉平台结合缩略图与视频标题的多模态特性，我们通过微调视觉语言模型，开发了一个多模态道德情绪分类器。该模型基于两种语言的人工标注多模态数据集进行训练，并应用于来自主要新闻机构的约40万个视频。我们分析了包括观看量、点赞量和评论量在内的参与度指标，这些指标代表了递增的参与程度。研究结果表明，谴责他者的道德义愤修辞表达——即从道德层面批评他人——在不同文化中均能持续提升所有形式的参与度，其影响范围从被动观看到主动评论。这些发现表明，道德义愤是一种特别有效的情感策略，不仅能吸引注意力，还能促进主动参与。我们讨论了谴责他者修辞可能被滥用的担忧，因为此类做法可能通过强化群体内与群体外对立而加剧极化。为促进未来研究并确保可复现性，我们公开发布了韩语和英语的多模态道德情绪分类器。