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

TL;DR

This study investigates how multimodal moral outrage in YouTube news videos influences audience engagement across Korea and the US, revealing that condemning rhetoric significantly boosts viewer interaction and participation.

Contribution

Develops a multimodal moral emotion classifier for Korean and English videos, analyzing its impact on audience engagement and highlighting the role of moral outrage in media influence.

Findings

Moral outrage increases all engagement types across cultures.

Condemning rhetoric effectively attracts active participation.

Releasing classifiers promotes reproducibility and future research.

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.