News media employ moral language to create memorable stories, and readers
often engage with the content that align with their values. Moral theories have
been applied to news analysis studying moral values in isolation, while the
intricate dynamics among participating entities in shaping moral events have
been overlooked. This is mainly due to the use of obscure language to conceal
evident ideology and values, coupled with the insufficient moral reasoning
capability in most existing NLP systems, where LLMs are no exception. To study
this phenomenon, we first annotate a new dataset, MORAL EVENTS, consisting of
5,494 structured annotations on 474 news articles by diverse US media across
the political spectrum. We further propose MOKA, a moral event extraction
framework with MOral Knowledge Augmentation, that leverages knowledge derived
from moral words and moral scenarios. Experimental results show that MOKA
outperforms competitive baselines across three moral event understanding tasks.
Further analyses illuminate the selective reporting of moral events by media
outlets of different ideological leanings, suggesting the significance of
event-level morality analysis in news. Our datasets and codebase are available
at this https URL

利用道德事件数据集和基于道德知识增强的 MOKA 框架，研究新闻报道中道德事件的选择性报道和不同意识形态倾向，揭示了新闻媒体运用道德语言创造记忆深刻的故事，并与读者价值观相契合的内在动力。