  News media often strive to minimize explicit moral language in news articles,
yet most articles are dense with moral values as expressed through the reported
events themselves. However, values that are reflected in the intricate dynamics
among participating entities and moral events are far more challenging for most
NLP systems to detect, including LLMs. To study this phenomenon, we annotate a
new dataset, MORAL EVENTS, consisting of 5,494 structured event 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, which leverages knowledge derived from moral words and moral
scenarios to produce structural representations of morality-bearing events.
Experiments show that MOKA outperforms competitive baselines across three moral
event understanding tasks. Further analysis shows even ostensibly nonpartisan
media engage in the selective reporting of moral events. Our data and codebase
are available at https://github.com/launchnlp/MOKA.
