# News Source Citing Patterns in AI Search Systems

arXiv: https://arxiv.org/abs/2507.05301 · HTML: https://mlsanity.com/paper/2507.05301/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 7 Jul 2025.

## Builds on (most cited first, 10 of 10)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
- [When Search Engine Services meet Large Language Models: Visions and Challenges](https://mlsanity.com/paper/2407.00128/) (2024)
- [Comparing Traditional and LLM-based Search for Consumer Choice: A Randomized Experiment](https://mlsanity.com/paper/2307.03744/) (2023)
- [The Use of Generative Search Engines for Knowledge Work and Complex Tasks](https://mlsanity.com/paper/2404.04268/) (2024)
- [Generative AI Search Engines as Arbiters of Public Knowledge: An Audit of Bias and Authority](https://mlsanity.com/paper/2405.14034/) (2024)
- [NELA-Local: A Dataset of U.S. Local News Articles for the Study of County-level News Ecosystems](https://mlsanity.com/paper/2203.08600/) (2022)
- [Search Engines Post-ChatGPT: How Generative Artificial Intelligence Could Make Search Less Reliable](https://mlsanity.com/paper/2402.11707/) (2024)
- [Human Trust in AI Search: A Large-Scale Experiment](https://mlsanity.com/paper/2504.06435/) (2025)
- [Social media sharing by political elites: An asymmetric American exceptionalism](https://mlsanity.com/paper/2207.06313/) (2022)

## Built on by (most cited first, 10 of 10)

- [Generative Engine Optimization at Scale: Measuring Brand Visibility Across AI Search Engines](https://mlsanity.com/paper/2606.20065/) (2026)
- [How Large Language Models Source Brand Reputation Across Languages and Markets](https://mlsanity.com/paper/2606.25787/) (2026)
- [Large Language Models Require Curated Context for Reliable Political Fact-Checking -- Even with Reasoning and Web Search](https://mlsanity.com/paper/2511.18749/) (2025)
- [The End of Rented Discovery: How AI Search Redistributes Power Between Hotels and Intermediaries](https://mlsanity.com/paper/2603.20062/) (2026)
- [Divergent Recommendations, Convergent Diagnoses: Cross-Provider Failure-Mode Convergence in AI Commercial Recommendation](https://mlsanity.com/paper/2606.26116/) (2026)
- [Measuring Brand and Source Discovery under Repeated LLM Queries: A Finite-Sample Audit](https://mlsanity.com/paper/2609.05059/) (2026)
- [Assessing Web Search Credibility and Response Groundedness in Chat Assistants](https://mlsanity.com/paper/2510.13749/) (2025)
- [When AI Writes, Who Gets Cited? Evidence of Citation Monoculture Across Language Models](https://mlsanity.com/paper/2608.19230/) (2026)
- [What Drives Citations in Production Large Language Models? An Observational Multi-Method Study of Two Million AI Citations Across Ten Thousand Web Pages](https://mlsanity.com/paper/2609.35077/) (2026)
- [What Drives AI Citations? Content, Retrieval and Model Knowledge in Generative Engine Optimization (GEO)](https://mlsanity.com/paper/2610.15033/) (2026)

## Abstract (arXiv)

AI-powered search systems are emerging as new information gatekeepers, fundamentally transforming how users access news and information. Despite their growing influence, the citation patterns of these systems remain poorly understood. We address this gap by analyzing data from the AI Search Arena, a head-to-head evaluation platform for AI search systems. The dataset comprises over 24,000 conversations and 65,000 responses from models across three major providers: OpenAI, Perplexity, and Google. Among the over 366,000 citations embedded in these responses, 9% reference news sources. We find that while models from different providers cite distinct news sources, they exhibit shared patterns in citation behavior. News citations concentrate heavily among a small number of outlets and display a pronounced liberal bias, though low-credibility sources are rarely cited. User preference analysis reveals that neither the political leaning nor the quality of cited news sources significantly influences user satisfaction. These findings reveal significant challenges in current AI search systems and have important implications for their design and governance.