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

该项目研究框架，这是政治传播中的一个核心概念，指的是从一个角度描绘一个问题，并相应地淡化相互竞争的观点。 众所周知，框架会显着影响公众对政策问题和政策结果的态度。 由于社交媒体允许公民更多地参与政治话语，对政治世界的科学研究需要对问题是如何构建的进行可靠的分析，不仅是传统媒体和精英，而且是参与公共话语的公民。然而，用于框架发现和分类的传统内容分析是复杂且劳动密集型的。此外，现有的方法不足以捕获一帧随着时间的推移演变为另一帧时的许多实例。因此，该项目开发了新的计算建模方法，以数据驱动的计算语言学为基础，旨在提高对政治精英、媒体和公众如何构建问题的科学理解。 政治科学家和计算机科学家之间的合作有四个目标：（a）开发用于半自动框架发现的新方法，从而由政治科学家指导计算模型？专业知识加速并增强他们的分析过程； (b) 开发基于自然语言处理的新算法，用于自动帧分析，产生可与可靠的人类编码员相媲美的可测量的准确结果； (c) 确定这些程序对易于理解的案例的有效性； (d) 使用多年来跨传统和社交媒体流的数据，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机制，并帮助预测公众舆论和政策的趋势。

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.