SoCS: Collaborative Research: Data-Driven, Computational Models for Discovery and Analysis of Framing

SoCS：协作研究：用于发现和分析框架的数据驱动计算模型

• 批准号: 1551192
• 负责人: Justin Gross
• 金额: $ 3.85万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1551192&HistoricalAwards=false
• 关键词: SoCS; Collaborative; Research; Data; Driven

This project studies framing, a central concept in political communication that refers to portraying an issue from one perspective with corresponding de-emphasis of competing perspectives. Framing is known to significantly influence public attitudes toward policy issues and policy outcomes. As social media allow greater citizen engagement in political discourse, scientific study of the political world requires reliable analysis of how issues are framed, not only by traditional media and elites but by citizens participating in public discourse. Yet conventional content analysis for frame discovery and classification is complex and labor-intensive. Additionally, existing methods are ill-equipped to capture those many instances when one frame evolves into another frame over time. This project therefore develops new computational modeling methods, grounded in data-driven computational linguistics, aimed at improving the scientific understanding of how issues are framed by political elites, the media, and the public. This collaboration between political scientists and computer scientists has four goals: (a) developing novel methods for semi-automated frame discovery, whereby computational models guided by political scientists? expert knowledge speed up and augment their analytical process; (b) developing novel algorithms based on natural language processing for automatic frame analysis, producing measurably accurate results comparable with reliable human coders; (c) establishing the validity of these processes on well-understood cases; and (d) applying these methods to several current policy issues, using data across years and across traditional and social media streams. The resulting evolutionary framing data will help unpack the mechanisms of framing and help predict trends in public opinion and policy.

该项目研究了框架，这是一个政治传播中的核心概念，它是指从一个角度描绘一个问题，并相应地反对竞争观点。 众所周知，框架会严重影响公众对政策问题和政策结果的态度。 随着社交媒体允许更大的公民参与政治话语，对政治世界的科学研究需要对问题的构成可靠分析，不仅是传统媒体和精英，而且由参加公共话语的公民进行。然而，用于框架发现和分类的常规内容分析是复杂且劳动密集型的。此外，现有的方法是不足以捕获一帧随着时间而变成另一个框架时的许多实例。因此，该项目开发了基于数据驱动的计算语言学的新计算建模方法，旨在改善政治精英，媒体和公众对问题的科学理解。 政治科学家与计算机科学家之间的这种合作有四个目标：（a）为半自动化框架发现开发新颖的方法，从而在政治科学家指导的计算模型中？专家知识加快并增加了他们的分析过程； （b）基于自然语言处理来开发新型算法，以进行自动框架分析，从而产生与可靠的人类编码者相当的准确结果； （c）确定这些过程对众所周知的案件的有效性； （d）使用多年以及传统和社交媒体流的数据应用这些方法，将这些方法应用于当前的几个政策问题。由此产生的进化框架数据将有助于解开框架的机制，并有助于预测公众舆论和政策的趋势。