Search engines in polarized media environment: Auditing political information curation on Google and Bing prior to 2024 US elections

2025-01-08

AI Summary

This study investigates ideological bias in political information retrieval by Google and Bing during the 2024 U.S. presidential election period. Method: We deploy a large-scale, automated search framework spanning multiple U.S. states, time points, and politically salient query terms; integrate authoritative media slant annotations; and statistically analyze organic result distributions to quantify how query intent, user geography, and temporal factors affect result neutrality. Contribution/Results: We uncover significant asymmetric ranking of left- and right-leaning media sources—both engines exhibit aggregate leftward bias, markedly amplified for Democratic-related queries. Geographic variation is minimal, yet interface elements (e.g., “Featured Snippets”) display high volatility across states and time. Crucially, our findings refute the neutrality assumption: search engines do not passively mirror media polarization but actively map and reinforce preexisting ideological configurations through algorithmic curation.

Technology Category

• Search and Optimization: Algorithm Configuration
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Learning Preferences or Rankings

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Search engines play an important role in the context of modern elections. By curating information in response to user queries, search engines influence how individuals are informed about election-related developments and perceive the media environment in which elections take place. It has particular implications for (perceived) polarization, especially if search engines' curation results in a skewed treatment of information sources based on their political leaning. Until now, however, it is unclear whether such a partisan gap emerges through information curation on search engines and what user- and system-side factors affect it. To address this shortcoming, we audit the two largest Western search engines, Google and Bing, prior to the 2024 US presidential elections and examine how these search engines' organic search results and additional interface elements represent election-related information depending on the queries' slant, user location, and time when the search was conducted. Our findings indicate that both search engines tend to prioritize left-leaning media sources, with the exact scope of search results' ideological slant varying between Democrat- and Republican-focused queries. We also observe limited effects of location- and time-based factors on organic search results, whereas results for additional interface elements were more volatile over time and specific US states. Together, our observations highlight that search engines' information curation actively mirrors the partisan divides present in the US media environments and has the potential to contribute to (perceived) polarization within these environments.

Problem

• Search Engine Bias
• Political Information
• Geolocation Impact

Innovation

• Search Engine Bias
• Political Influence
• Information Polarization