Moral Outrage Shapes Commitments Beyond Attention: Multimodal Moral Emotions on YouTube in Korea and the US

2026-01-29

AI Summary

This study investigates how mainstream news media on YouTube leverage moral emotions—particularly moral outrage—to influence users’ full-spectrum engagement behaviors, from viewing and liking to commenting, and examines the underlying mechanisms across cultural contexts. Leveraging a fine-tuned vision-language model, the authors develop a multimodal moral emotion classifier by integrating video titles and thumbnails, enabling large-scale empirical analysis of approximately 400,000 news videos from South Korea and the United States. The research quantifies, for the first time, the facilitative effect of moral condemnation rhetoric on deep user engagement across cultures, demonstrating its significant and culturally robust enhancement of participation at all levels, while also uncovering its potential to exacerbate group polarization. The project releases the first bilingual (Korean–English) multimodal moral emotion classifier to support reproducible research.

Technology Category

• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Multimodal Learning
• Application Domains: Humanities & Computational Social Science

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• moral outrage
• audience engagement
• multimodal emotion
• media rhetoric
• polarization

Innovation

• multimodal moral emotion classifier
• vision-language model
• fine-tuning
• cross-cultural analysis
• YouTube engagement