ABSTRACT

Framing is one of the most central, applicable, and contested theories in communication research. At the heart of the debate on framing is the question of operationalizing and measuring emphasis frames. We harness novel computational tools to propose a new method for inductive identification of frames. We argue and demonstrate that frame elements could be identified using topic modeling, and that frame elements can then be automatically grouped into frame “packages” using community detection techniques applied to the topic network. Building upon recent conceptual and methodological developments in framing research, we introduce a new approach, the Analysis of Topic Model Networks (ANTMN). We demonstrate the applicability of our method in case studies where framing theory is developed and fairly consistent, and in exploratory ones where it is not, using three diverse U.S. news corpora: the coverage of political candidates in Senate races (n = 8,337 articles), foreign nations (n = 18,216), and infectious diseases and epidemics (n = 5,005). We conclude by discussing the theoretical, methodological, and practical implications of ANTMN.

摘要

框架是传播研究中最核心，最适用和最有争议的理论之一。框架辩论的核心是操作和衡量重点框架的问题。我们利用新颖的计算工具来提出一种新的框架归纳识别方法。我们争论并证明，可以使用主题建模来识别框架元素，然后可以使用应用于主题网络的社区检测技术将框架元素自动分组为框架“包装”。在框架研究的最新概念和方法发展的基础上，我们引入了一种新方法，主题模型网络分析（ANTMN）。我们证明了我们的方法在框架理论发展且相当一致的案例研究中的适用性，在没有框架理论的探索性研究中的适用性，使用三种不同的美国新闻语料库：参议院竞选中的政治候选人报道（n = 8,337篇文章），外国（n = 18,216）以及传染病和流行病（n = 5,005）。最后，我们讨论了ANTMN的理论，方法和实践意义。