Dictionaries, Supervised Learning, and Media Coverage of Public Policy
Dictionaries, Supervised Learning, and Media Coverage of Public Policy
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词典、监督学习和公共政策的媒体报道
DOI：
10.1080/10584609.2020.1763529
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发表时间：
2020
影响因子：
7.5
通讯作者：
Wlezien, Christopher
中科院分区：
文献类型：
--
作者：
Dun, Lindsay;Soroka, Stuart;Wlezien, Christopher
自动化内容分析有许多不同的方法。本文的重点是词典和监督学习；除了比较两者的有效性外，我们还论证了将两者结合使用的优势。我们在一个研究领域这样做，在这个领域，我们有一个独立的目标参照物：政府支出。为了捕捉媒体对公共政策报道的准确性，我们应用分层词典计数和监督学习来衡量大众媒体对美国国防开支变化的报道。这两种方法似乎都很好地捕捉到了该地区的媒体“政策信号”，这为融合有效性提供了一个重要的测试。虽然结果强调了单独使用词典和机器学习方法的价值，但它们也说明了这两种方法可以结合使用的方式。
There are many different approaches to automated content analysis. This paper focuses on dictionaries and supervised learning; in addition to comparing the effectiveness of the two, we argue for the advantages of using them in combination. We do so in a research area in which we have an independent objective referent: government spending. With an eye toward capturing the accuracy of media coverage on public policy, we apply both hierarchical dictionary counts and supervised learning to measure mass media coverage of change in US defense spending. Both approaches appear to do well at capturing a media “policy signal” in the area, which provides an important test of convergent validity. While the results highlight the value of both dictionary and machine learning methods used independently, they also illustrate ways in which the two can be used in combination.