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: 2127746
• 负责人: Ruihong Huang
• 金额: $ 22.06万
• 依托单位: Texas A&M Engineering Experiment Station
• 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=2127746&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，而公众舆论又受到人们阅读、观看和收听的新闻的显着影响。因此，知情公众必须了解他们所消费的新闻是如何被选择、包装和呈现的。该项目旨在构建计算系统，以检测和量化媒体意识形态如何在文章及其组成事件层面影响新闻的创作和呈现。该项目将提高新闻制作的透明度并提高公众对媒体决策的认识。开发的工具可以有效和高效地支持组织和文章层面的媒体意识形态测量，从而促进政治学、社会科学和传播等广泛领域的研究。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模块的基础。该项目产生的知识将通过演示、发表的博客、播客演讲以及报纸上的客座文章向公众传播。该项目将研究通过新闻文章中呈现的内容的选择和组织来包装新闻如何导致媒体偏见，并开发实体和事件驱动的计算模型来检测意识形态内容选择和预测文章级意识形态。将探讨三个主要研究任务。首先，将开发话语感知事件分类模型，以区分主要事件的描述与其他上下文通知事件和间接相关事件。其次，将建立一个实体和事件驱动的上下文表示学习框架，通过捕获实体和事件之间的关系来检测媒体偏见。第三，将研究对抗性学习，通过将媒体特定语言与意识形态内容分开，以细粒度的分数来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审查标准进行评估，被认为值得支持。

项目成果

Semi-supervised News Discourse Profiling with Contrastive Learning

• DOI: 10.48550/arxiv.2309.11692
• 发表时间: 2023-09
• 作者: Ming Li;Ruihong Huang

Sentence-level Media Bias Analysis Informed by Discourse Structures

• DOI: 10.18653/v1/2022.emnlp-main.682
• 发表时间: 2022
• 期刊: Bioresources and Bioprocessing
• 影响因子: 4.6
• 作者: Yuanyuan Lei;Ruihong Huang;Lu Wang;Nick Beauchamp

Profiling News Discourse Structure Using Explicit Subtopic Structures Guided Critics

• DOI: 10.18653/v1/2021.findings-emnlp.137
• 发表时间: 2021
• 作者: Prafulla Kumar Choubey;Ruihong Huang