Most previous research on moral frames has focused on social media short
texts, little work has explored moral sentiment within news articles. In news
articles, authors often express their opinions or political stance through
moral judgment towards events, specifically whether the event is right or wrong
according to social moral rules. This paper initiates a new task to understand
moral opinions towards events in news articles. We have created a new dataset,
EMONA, and annotated event-level moral opinions in news articles. This dataset
consists of 400 news articles containing over 10k sentences and 45k events,
among which 9,613 events received moral foundation labels. Extracting event
morality is a challenging task, as moral judgment towards events can be very
implicit. Baseline models were built for event moral identification and
classification. In addition, we also conduct extrinsic evaluations to integrate
event-level moral opinions into three downstream tasks. The statistical
analysis and experiments show that moral opinions of events can serve as
informative features for identifying ideological bias or subjective events.

本文介绍了一项新的任务，即理解新闻文章中对事件的道德观点。通过创建一个名为 EMONA 的新数据集，对新闻文章中的事件级道德观点进行了注释，并建立了用于事件道德识别和分类的基准模型。实验证明，对事件的道德观点可以用于识别意识形态偏见或主观事件。

EMONA: 新闻文章中的事件级道德观点

EMONA: Event-level Moral Opinions in News Articles

News outlets are a primary source for many people to learn what is going on
in the world. However, outlets with different political slants, when talking
about the same news story, usually emphasize various aspects and choose their
language framing differently. This framing implicitly shows their biases and
also affects the reader's opinion and understanding. Therefore, understanding
the framing in the news stories is fundamental for realizing what kind of view
the writer is conveying with each news story. In this paper, we describe
methods for characterizing moral frames in the news. We capture the frames
based on the Moral Foundation Theory. This theory is a psychological concept
which explains how every kind of morality and opinion can be summarized and
presented with five main dimensions. We propose an unsupervised method that
extracts the framing Bias and the framing Intensity without any external
framing annotations provided. We validate the performance on an annotated
twitter dataset and then use it to quantify the framing bias and partisanship
of news.

该研究论文描述了一种基于道德基础理论的无监督方法，用于在新闻报道中提取道德框架的偏见和强度，以量化新闻报道的偏见和党派性，并验证了该方法在 Twitter 数据集上的性能。