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Computationally Intensive Content Analysis of Public Diplomacy Data: Understanding the Public Remarks of US Secretaries of State, 1997-2011

机译:公共外交数据的计算密集型内容分析:了解美国国务卿的公开言论,1997-2011年

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This paper continues to test the ASSANA methodology for the computer-assisted analysis of large-scale, unstructured, text-based data in international affairs research. Traditional methodologies for exploring the role of the Secretary of State in projecting "soft power" have focused on reading and analyzing texts to uncover important themes and patterns. However, the increasingly large volume of data available presents a significant challenge for these traditional methods of analysis. We examine three data sets consisting of every remark by Secretary Clinton since taking office (n=2, 438), Secretary Rice from 2005-2009 (n=1, 766), and Secretary Albright from 1997-2001 (n=1, 335). We use computer-assisted content analysis to find key themes for each Secretary and search for similarities between key themes and phrases. We find a limited number of similar keywords across the dataset, except International and Issues. However, the following phrases are shared between all Secretaries: Human Rights, Foreign Policy, Middle East, International Community, and North Korea.
机译:本文继续测试ASSANA方法论,以在国际事务研究中对大规模,非结构化,基于文本的数据进行计算机辅助分析。探索国务卿在投射“软实力”中的作用的传统方法论着重于阅读和分析文本以发现重要的主题和模式。但是,对于这些传统的分析方法,可用的数据量越来越大提出了巨大的挑战。我们研究了三个数据集,包括克林顿上任以来的每句话(n = 2,438),2005-2009年的莱斯部长(n = 1,766)和1997-2001的奥尔布赖特秘书(n = 1,335)。 )。我们使用计算机辅助内容分析来查找每个秘书的关键主题,并搜索关键主题和短语之间的相似性。我们在整个数据集中发现了数量有限的相似关键字,但“国际”和“问题”除外。但是,所有秘书之间都共享以下短语:人权,外交政策,中东,国际社会和朝鲜。

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