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 https://github.com/launchnlp/MOKA.

翻译：新闻媒体运用道德语言来创造令人难忘的故事，读者往往倾向于接触与其价值观相符的内容。现有道德理论虽已应用于新闻分析，但仅孤立地研究道德价值观，忽视了参与实体在塑造道德事件过程中的复杂动态关系。这主要是由于模糊语言掩盖了显性的意识形态和价值观，加之现有自然语言处理系统（包括大语言模型在内）普遍缺乏足够的道德推理能力。为探究这一现象，我们首先标注了一个新数据集MORAL EVENTS，包含来自美国跨政治光谱不同媒体的474篇新闻文章中5,494条结构化标注。我们进一步提出MOKA——一种基于道德知识增强的道德事件抽取框架，该框架利用从道德词汇和道德场景中获取的知识。实验结果表明，MOKA在三个道德事件理解任务上均优于具有竞争力的基线方法。进一步分析揭示了不同意识形态倾向的媒体机构对道德事件的选择性报道，凸显了新闻中事件级道德分析的重要性。我们的数据集和代码库可在 https://github.com/launchnlp/MOKA 获取。