Measuring Variety, Balance, and Disparity: An Analysis of Media Coverage of the 2021 German Federal Election
This addresses the need to prevent filter bubbles and inform public discourse, particularly around elections, though it is incremental as it builds on existing diversity measurement methods.
The paper tackled the problem of measuring diversity in news articles holistically across variety, balance, and disparity, and applied this framework to analyze over 26,000 headlines from the 2021 German federal election, finding high diversity for general terms but mixed results for specific topics.
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.