AI Citation Research

Research on AI citations

Comprehensive studies on how LLMs cite and attribute content across the web

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Featured Research

Citation Behaviour

LLM Content Attribution Patterns

Analysis of how different AI models structure citations and references when generating responses about brands and websites.

March 20248 min read
4 LLMs analyzedRead
Provider Analysis

ChatGPT vs Perplexity Citation Styles

Comparative study examining citation frequency, format, and context differences between major AI providers.

February 202412 min read
2,000+ responsesRead
Industry Benchmarks

Domain Authority and Citation Rates

Research into how website authority metrics correlate with AI citation frequency across different industries.

January 202410 min read
500 domains trackedRead
Citation Behaviour

Temporal Patterns in AI Citations

How citation patterns change over time and the factors that influence AI models to reference newer vs older content.

December 20236 min read
6-month studyRead
Provider Analysis

Gemini and Claude Content Attribution

Examining how Google's Gemini and Anthropic's Claude approach source attribution compared to established providers.

November 20239 min read
Cross-model studyRead
Industry Benchmarks

Enterprise vs Consumer Citation Patterns

How AI models cite B2B vs B2C websites differently and implications for content strategy optimization.

October 202311 min read
B2B/B2C analysisRead

Research Findings

5
LLMs analyzed

ChatGPT, Perplexity, Gemini, Claude, and emerging providers across comprehensive testing scenarios

12,000+
Citations sampled

Large-scale analysis across multiple industries, query types, and content formats for statistical significance

60%
Top-10 source bias

AI models show preference for high-authority sources, with 60% of citations coming from top-10 ranked domains

Research Methodology

Our research methodology combines automated testing with manual validation to provide accurate insights into AI citation behaviour. We use controlled experiments to ensure reproducible results and eliminate bias in our findings.

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Systematic Sampling

Standardized queries across multiple AI providers with consistent parameters to ensure comparable results

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Statistical Analysis

Advanced text analysis and citation pattern detection with confidence intervals and significance testing

Validation Protocol

Manual review of automated findings by domain experts to ensure accuracy and contextual understanding

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