Measuring Variety, Balance, and Disparity: An Analysis of Media Coverage of the 2021 German Federal Election

TL;DR

This paper introduces a comprehensive framework to measure news diversity across variety, balance, and disparity, applied to a large dataset of German election coverage, revealing nuanced differences based on search terms.

Contribution

It presents a novel holistic framework for measuring news diversity and provides a new dataset of German election news articles for analysis.

Findings

High diversity observed for general search terms

Specific topics show high diversity in two out of three dimensions

Diversity varies with the subject matter and search terms

Abstract

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.