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 three engagement levels (views, likes, and comments), representing increasing degrees of commitment. The results show that other-condemning rhetoric—expressions of moral outrage that criticize others' morality—consistently increases all forms of engagement across cultures, with effect sizes strengthening 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/out-group divisions. To facilitate future research and ensure reproducibility, we publicly release our Korean and English multimodal moral emotion classifiers.

Many post-hoc explainable feature attribution techniques analyze gradient propagation and understand decisions of deep learning models. However, conventional gradient analysis used to generate such maps can be noisy, potentially compromising the reliability of explanations. In this work, we introduce a robust method for computing feature attributions by identifying critical parameters and refining gradient propagation through these parameters. This method reduces the impact of non-critical parameters, mitigating the effect of feature leakage and randomized initialization, which introduce noise in attribution maps. We implemented this concept as an add-on module, called CriGrad , and evaluated its efficacy using three benchmarks and seven explainable models. Our results show that focusing on critical parameters improves explanability in 93% of the cases, demonstrating its effectiveness and improved reliability.