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News media employ moral language to create memorable stories, and readersoften engage with the content that align with their values. Moral theories havebeen applied to news analysis studying moral values in isolation, while theintricate dynamics among participating entities in shaping moral events havebeen overlooked. This is mainly due to the use of obscure language to concealevident ideology and values, coupled with the insufficient moral reasoningcapability in most existing NLP systems, where LLMs are no exception. To studythis phenomenon, we first annotate a new dataset, MORAL EVENTS, consisting of5,494 structured annotations on 474 news articles by diverse US media acrossthe political spectrum. We further propose MOKA, a moral event extractionframework with MOral Knowledge Augmentation, that leverages knowledge derivedfrom moral words and moral scenarios. Experimental results show that MOKAoutperforms competitive baselines across three moral event understanding tasks.Further analyses illuminate the selective reporting of moral events by mediaoutlets of different ideological leanings, suggesting the significance ofevent-level morality analysis in news. Our datasets and codebase are availableat https://github.com/launchnlp/MOKA.

MOKA: Moral Knowledge Augmentation for Moral Event Extraction