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.

新闻文章中的多样性测量对于多个原因至关重要，包括防止信息茧房和加强公共话语，尤其是在选举之前。本文提出了一个基于多样性维度的新闻文章多样性测量框架，并提供了一个以 2021 年德国联邦选举前后两周内收集的超过 900 个新闻媒体的 26,000 多个独特标题组成的谷歌头条新闻数据集。研究结果表明，对于更一般的搜索词（如 “选举”）具有较高的多样性，而一系列其他相关搜索词（如 “教育”、“欧洲”、“气候保护”、“政府”）在三个维度中有高多样性，反映出对于更具未来导向的主题有更主观、专注的讨论。