MOKA: Moral Knowledge Augmentation for Moral Event Extraction
Xinliang Frederick Zhang Winston Wu Nick Beauchamp Lu Wang
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.