EMONA: Annotating Event-level Moral Opinions in News Articles

Conceitos Básicos

This paper introduces a new dataset, EMONA, that annotates event-level moral opinions in news articles. The dataset provides a fine-grained understanding of how journalists express moral judgments towards events to shape public opinions.

Resumo

The paper introduces a new dataset called EMONA that annotates event-level moral opinions in news articles. The key highlights are:

Most previous research on moral frames has focused on social media short texts, but 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.

The EMONA dataset consists of 400 news articles containing over 10k sentences and 45k events, among which 9,613 events received moral foundation labels. Annotating event-level moral opinions is a challenging task as moral judgment towards events can be very implicit.

The dataset analysis shows that event-level moral opinions can effectively reflect article-level ideology, designate sentence-level political bias, and uncover implicit event-level opinions.

Baseline models are built for event moral identification and classification tasks. Extrinsic evaluations on three downstream tasks (article-level ideology classification, sentence-level media bias identification, and event-level opinion identification) demonstrate the usefulness of detecting event-level moral opinions.

The analysis and experiments validate the value of the new EMONA dataset for studying the role of moral opinions in news media.

Estatísticas

"More than 200 people crowded in the forum on Friday." (Event: crowded, Moral: non-moral)
"We show empathy for other people who might choose abortion." (Events: empathy, choose, abortion; Morals: care, non-moral, non-moral)
"Mayor asked New Yorkers to report after the execution-style killing." (Events: asked, report, killing; Morals: authority, non-moral, harm)

Citações

"Morality refers to a set of social moral principles to distinguish between right and wrong."
"In news media, the authors often express their stance through moral judgment towards events, so as to shape public opinions."
"Annotating event-level moral opinions within a news article turned out to be a challenging and demanding task for human annotators."

Principais Insights Extraídos De

EMONA

by Yuanyuan Lei... às arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01715.pdf

Perguntas Mais Profundas

How can the event-level moral opinions be leveraged to improve downstream NLP tasks beyond the ones evaluated in this paper?

Event-level moral opinions can be leveraged to enhance various downstream NLP tasks by providing a deeper understanding of the moral framing and sentiment surrounding events in news articles. Some ways to leverage event-level moral opinions include:

Sentiment Analysis: Incorporating event-level moral opinions can enrich sentiment analysis by capturing the moral dimensions of sentiment. This can help in identifying nuanced sentiments and understanding the underlying moral values associated with different events.

Stance Detection: Event-level moral opinions can aid in stance detection by revealing the ideological biases and moral perspectives present in the text. This can help in identifying the stance of the author or source towards specific events.

Fake News Detection: By analyzing the moral judgments associated with events, it may be possible to detect misleading or biased information in news articles. Event-level moral opinions can serve as indicators of potential misinformation or manipulation.

Event Summarization: Integrating event-level moral opinions into event summarization tasks can provide a more comprehensive summary that includes the moral framing and ethical considerations surrounding key events in news articles.

Opinion Mining: Event-level moral opinions can be valuable in opinion mining tasks to extract and analyze the moral viewpoints expressed in news articles. This can help in understanding public opinions and societal values related to different events.

What are the potential biases or limitations in the EMONA dataset, and how can they be addressed in future work?

Potential biases or limitations in the EMONA dataset may include:

Annotation Bias: Human annotators may have subjective interpretations of moral opinions, leading to potential annotation biases. To address this, future work could involve additional rounds of annotation with diverse annotators to ensure a more comprehensive and unbiased dataset.

Imbalanced Data: The dataset may have imbalanced distributions of moral labels, with certain moral dimensions being more prevalent than others. Techniques such as data augmentation, oversampling, or using advanced modeling approaches can help mitigate this imbalance.

Implicit Moral Opinions: Event-level moral opinions can be implicit and context-dependent, making them challenging to annotate accurately. Future work could explore advanced natural language processing techniques to capture subtle moral nuances and implicit opinions more effectively.

Generalization: The dataset may be limited in its coverage of diverse news topics and sources, potentially affecting the generalizability of models trained on EMONA. Including a wider range of news articles from various domains and sources can help address this limitation.

How can the insights from event-level moral opinions be combined with other forms of contextual information to gain a more holistic understanding of news media bias and framing?

To gain a more holistic understanding of news media bias and framing, insights from event-level moral opinions can be combined with other contextual information in the following ways:

Contextual Semantics: Integrating event-level moral opinions with contextual semantics can provide a deeper understanding of how moral values influence the framing of news articles. By analyzing the interplay between language semantics and moral judgments, a more nuanced perspective on media bias can be achieved.

Multi-modal Analysis: Combining textual analysis with visual and audio data from news articles can offer a comprehensive view of media bias and framing. By incorporating multi-modal information, researchers can uncover subtle cues and patterns that contribute to biased reporting.

Network Analysis: Examining the relationships between events, moral opinions, and entities mentioned in news articles through network analysis can reveal underlying patterns of bias and framing. By visualizing these connections, researchers can identify key influencers and narratives shaping media discourse.

Temporal Analysis: Considering the temporal aspect of news coverage and how moral opinions evolve over time can provide insights into the dynamic nature of media bias. Analyzing changes in moral framing and opinions across different time periods can highlight shifting narratives and biases in news reporting.

By integrating event-level moral opinions with these additional contextual dimensions, researchers can develop a more comprehensive framework for analyzing news media bias and framing, leading to a deeper understanding of the complex interplay between moral values, language, and media representation.