Auditing Google's Search Algorithm: Measuring News Diversity Across Brazil, the UK, and the US

TL;DR

This paper audits Google's search algorithm across Brazil, the UK, and the US, revealing significant news source concentration, slight political bias, and tendencies to favor popular and recent outlets, impacting news diversity.

Contribution

It provides the first large-scale, cross-country analysis of news diversity in Google search results using algorithm auditing techniques and comprehensive datasets.

Findings

Significant concentration of news sources in search results.

Slight leftward political bias detected.

Preference for popular and recent news outlets.

Abstract

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.