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

这个项目研究的是框架，这是政治交流中的一个核心概念，指的是从一个角度描绘一个问题，同时相应地淡化相互竞争的观点。众所周知，框架会显著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可靠地分析问题是如何构成的，不仅是传统媒体和精英，而且是参与公共话语的公民。然而，用于帧发现和分类的传统内容分析是复杂和劳动密集型的。此外，当一个帧随时间演变为另一个帧时，现有方法不足以捕获这些实例。因此，该项目开发了以数据驱动的计算语言学为基础的新的计算建模方法，旨在提高对政治精英、媒体和公众如何制定问题的科学理解。政治学家和计算机科学家之间的合作有四个目标：(A)开发半自动框架发现的新方法，从而在政治学家的指导下建立计算模型？(B)开发基于自然语言处理的新算法，用于自动框架分析，产生可与可靠的人类编码员媲美的可衡量的准确结果；(C)确定这些过程在众所周知的案例中的有效性；以及(D)利用多年来以及跨传统和社交媒体的数据，将这些方法应用于几个当前的政策问题。由此产生的演变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。

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.