Collaborative Research: III: Small: Entity- and Event-driven Media Bias Detection

协作研究：III：小型：实体和事件驱动的媒体偏差检测

• 批准号: 2127747
• 负责人: Lu Wang
• 金额: $ 26.11万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2127747&HistoricalAwards=false
• 关键词: Collaborative; Research; III; Small; Entity

Democracy is shaped by public opinion, and public opinion in turn is significantly influenced by the news that is read, watched, and listened to. It is thus essential for an informed public to understand how the news they consume is being selected, packaged, and presented. This project aims to build computational systems to detect and quantify how media ideology affects the creation and presentation of news at the level of articles and their constituent events. This project will promote the transparency of news production and enhance public awareness of media decisions. The developed tools can effectively and efficiently support the measurement of media ideology at organization- and article-levels, which facilitates research in broad areas, including political science, social science, and communications. The proposed research will involve graduate and undergraduate students from a diverse array of backgrounds, especially underrepresented groups. The developed datasets and methods will form the basis of modules in newly developed courses. The knowledge produced in the project will be distributed to the public via demos, published blogs, talks at podcasts, and guest essays to newspapers. This project will examine how media bias can result from the packaging of news via the selection and organization of contents presented in news articles, and develop entity- and event-driven computational models for detecting ideological content selection and predicting article-level ideology. Three main research tasks will be explored. First, discourse-aware event categorization models will be developed to distinguish descriptions of main events from other context-informing events and indirectly-related events. Second, an entity- and event-driven contextual representation learning framework will be built to detect media bias by capturing relations between entities and events. Third, adversarial learning will be investigated to predict the political ideology of a news article with a fine-grained score by disentangling media-specific languages from ideological content.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

民主是由公众舆论塑造的，而公众舆论反过来又受到人们阅读、观看和收听的新闻的重大影响。因此，对于知情的公众来说，了解他们消费的新闻是如何被选择、包装和呈现的至关重要。该项目旨在建立计算系统，以检测和量化媒体意识形态如何影响文章及其组成事件层面的新闻创作和呈现。该项目将提高新闻制作的透明度，提高公众对媒体决定的认识。所开发的工具可以有效和高效地支持媒体意识形态的测量在组织和文章的水平，这有利于在广泛的领域，包括政治学，社会科学和通信的研究。拟议的研究将涉及来自不同背景的研究生和本科生，特别是代表性不足的群体。开发的数据集和方法将构成新开发课程中模块的基础。该项目产生的知识将通过演示、发表博客、播客讲座和报纸客座文章向公众传播。该项目将研究如何通过选择和组织新闻文章中呈现的内容来包装新闻，并开发实体和事件驱动的计算模型，用于检测意识形态内容选择和预测文章层面的意识形态。将探讨三项主要研究任务。首先，语篇感知的事件分类模型将被开发来区分其他上下文通知事件和间接相关的事件的主要事件的描述。其次，一个实体和事件驱动的上下文表征学习框架将建立检测媒体偏见捕捉实体和事件之间的关系。第三，对抗性学习将被研究，通过从意识形态内容中分离出媒体特有的语言，以细粒度的分数预测新闻文章的政治意识形态。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响力审查标准进行评估，被认为值得支持。

项目成果

Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation

• DOI: 10.48550/arxiv.2211.01467
• 发表时间: 2022-11
• 作者: Xinliang Frederick Zhang;Nick Beauchamp;Lu Wang

Late Fusion with Triplet Margin Objective for Multimodal Ideology Prediction and Analysis

• DOI: 10.48550/arxiv.2211.02269
• 发表时间: 2022-11
• 作者: Changyuan Qiu;Winston Wu;Xinliang Frederick Zhang;Lu Wang