EMONA: Event-level Moral Opinions in News Articles

TL;DR

This paper introduces EMONA, a new dataset and task for identifying moral opinions towards events in news articles, demonstrating their usefulness in understanding ideological bias and subjective content.

Contribution

The paper creates EMONA, a novel dataset with event-level moral annotations in news articles, and develops baseline models for moral opinion detection.

Findings

Event-level moral opinions can indicate ideological bias.

Moral features improve subjective event detection.

Baseline models achieve promising results on EMONA.

Abstract

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.