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.