ABSTRACT

Many researchers consider the presentation of diverse content as a prerequisite for the news media to fully exercise their democratic mandate. While prior news diversity studies have contributed important theoretical insights, we argue here that scholarly knowledge of this concept can be significantly advanced by employing computational methods for text analysis. Using automated methods, researchers can increase both the scope of data being analyzed and the resolution of the analysis. This article presents a novel framework for analyzing news diversity consisting of two distinct stages. In the first stage, a computational text classification method is used to analyze, at a high resolution, the attention given in news texts to a broad range of political and social issues. In the second stage, the text classifications are aggregated, and the distributions of media attention to those issues (i.e., news diversity) are assessed on a large scale. After presenting the novel approach, we illustrate its usefulness for testing theoretical hypotheses about news diversity. We compare the diversity of economic coverage in three elite and three popular US newspapers (N = 252,807 articles) and find that a fine-grained analysis relaxes concerns raised in previous studies about low content diversity in the popular press.

摘要

许多研究人员认为，呈现多样的内容是新闻媒体充分行使其民主职责的前提。尽管先前的新闻多样性研究为重要的理论见解做出了贡献，但在这里我们认为，通过采用计算方法进行文本分析可以大大提高对该概念的学术知识。使用自动化方法，研究人员可以扩大被分析数据的范围和分析的分辨率。本文提出了一个新颖的框架，用于分析新闻多样性，包括两个不同的阶段。在第一阶段，使用一种计算文本分类方法来高分辨率分析新闻文本对广泛的政治和社会问题的关注。在第二阶段，汇总文本分类，大规模评估了媒体对这些问题的关注度（即新闻多样性）。在介绍了新颖的方法之后，我们说明了其在测试有关新闻多样性的理论假设方面的有用性。我们比较了三大精英报纸和三大美国流行报纸的经济报道多样性（N = 252,807篇文章），并且发现细粒度的分析可以缓解先前研究中有关大众媒体中低含量多样性的关注。