mlsanityData to 14 Oct 2026

News Source Citing Patterns in AI Search Systems

arXiv 2507.05301 ↗First posted 7 Jul 2025cs.IR cs.CL cs.CYcited by 10

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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.

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

10 of 36 references.

References · 10

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2024Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preferencecited by 1,184
  3. 2024When Search Engine Services meet Large Language Models: Visions and Challengescited by 59
  4. 2023Comparing Traditional and LLM-based Search for Consumer Choice: A Randomized Experimentcited by 36
  5. 2024The Use of Generative Search Engines for Knowledge Work and Complex Taskscited by 18
  6. 2024Generative AI Search Engines as Arbiters of Public Knowledge: An Audit of Bias and Authoritycited by 13
  7. 2022NELA-Local: A Dataset of U.S. Local News Articles for the Study of County-level News Ecosystemscited by 6
  8. 2024Search Engines Post-ChatGPT: How Generative Artificial Intelligence Could Make Search Less Reliablecited by 6
  9. 2025Human Trust in AI Search: A Large-Scale Experimentcited by 3
  10. 2022Social media sharing by political elites: An asymmetric American exceptionalismcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 8.

10 papers.

  1. 2026Generative Engine Optimization at Scale: Measuring Brand Visibility Across AI Search Enginescited by 3
  2. 2026How Large Language Models Source Brand Reputation Across Languages and Marketscited by 3
  3. 2025Large Language Models Require Curated Context for Reliable Political Fact-Checking -- Even with Reasoning and Web Searchcited by 2
  4. 2026The End of Rented Discovery: How AI Search Redistributes Power Between Hotels and Intermediariescited by 1
  5. 2026Divergent Recommendations, Convergent Diagnoses: Cross-Provider Failure-Mode Convergence in AI Commercial Recommendationcited by 1
  6. 2026Measuring Brand and Source Discovery under Repeated LLM Queries: A Finite-Sample Auditcited by 1
  7. 2025Assessing Web Search Credibility and Response Groundedness in Chat Assistantscited by 0
  8. 2026When AI Writes, Who Gets Cited? Evidence of Citation Monoculture Across Language Modelscited by 0
  9. 2026What Drives Citations in Production Large Language Models? An Observational Multi-Method Study of Two Million AI Citations Across Ten Thousand Web Pagescited by 0
  10. 2026What Drives AI Citations? Content, Retrieval and Model Knowledge in Generative Engine Optimization (GEO)cited by 0