Auditing Google's Search Algorithm: Measuring News Diversity Across Brazil, the UK, and the US
It addresses how Google's algorithm may reinforce media inequalities, impacting public access to diverse news, but is incremental in auditing methods.
This study measured news diversity in Google search results across Brazil, the UK, and the US, finding significant concentration favoring popular and national outlets with a slight leftward bias, based on an analysis of 221,863 search results.
This study examines the influence of Google's search algorithm on news diversity by analyzing search results in Brazil, the UK, and the US. It explores how Google's system preferentially favors a limited number of news outlets. Utilizing algorithm auditing techniques, the research measures source concentration with the Herfindahl-Hirschman Index (HHI) and Gini coefficient, revealing significant concentration trends. The study underscores the importance of conducting horizontal analyses across multiple search queries, as focusing solely on individual results pages may obscure these patterns. Factors such as popularity, political bias, and recency were evaluated for their impact on news rankings. Findings indicate a slight leftward bias in search outcomes and a preference for popular, often national outlets. This bias, combined with a tendency to prioritize recent content, suggests that Google's algorithm may reinforce existing media inequalities. By analyzing the largest dataset to date -- 221,863 search results -- this research provides comprehensive, longitudinal insights into how algorithms shape public access to diverse news sources.