  Determining and measuring diversity in news articles is important for a
number of reasons, including preventing filter bubbles and fueling public
discourse, especially before elections. So far, the identification and analysis
of diversity have been illuminated in a variety of ways, such as measuring the
overlap of words or topics between news articles related to US elections.
However, the question of how diversity in news articles can be measured
holistically, i.e., with respect to (1) variety, (2) balance, and (3)
disparity, considering individuals, parties, and topics, has not been
addressed. In this paper, we present a framework for determining diversity in
news articles according to these dimensions. Furthermore, we create and provide
a dataset of Google Top Stories, encompassing more than 26,000 unique headlines
from more than 900 news outlets collected within two weeks before and after the
2021 German federal election. While we observe high diversity for more general
search terms (e.g., "election"), a range of search terms ("education,"
"Europe," "climate protection," "government") resulted in news articles with
high diversity in two out of three dimensions. This reflects a more subjective,
dedicated discussion on rather future-oriented topics.
