mlsanityData to 14 Oct 2026

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

arXiv 2601.21815 ↗First posted 29 Jan 2026cs.CY cs.AI cs.CLcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 4 of its 43 references that are in the map. First posted on arXiv 29 Jan 2026.

Builds on

4 of 43 references.

References · 4

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
  3. 2022Multimodal Learning with Transformers: A Surveycited by 400
  4. 2022Moral Emotions Shape the Virality of COVID-19 Misinformation on Social Mediacited by 12

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

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  4. 2025Are Rules Meant to be Broken? Understanding Multilingual Moral Reasoning as a Computational Pipeline with UniMoral
  5. 2025Normative Evaluation of Large Language Models with Everyday Moral Dilemmas
  6. 2025Visual Authority and the Rhetoric of Health Misinformation: A Multimodal Analysis of Social Media Videos
  7. 2025MFTCXplain: A Multilingual Benchmark Dataset for Evaluating the Moral Reasoning of LLMs through Multi-hop Hate Speech Explanation
  8. 2026Multimodal Large Language Models as Synthetic Participants in Video-Based Studies: An Evaluation