LLM Content Attribution Patterns
Analysis of how different AI models structure citations and references when generating responses about brands and websites.
Comprehensive studies on how LLMs cite and attribute content across the web
Explore ResearchAnalysis of how different AI models structure citations and references when generating responses about brands and websites.
Comparative study examining citation frequency, format, and context differences between major AI providers.
Research into how website authority metrics correlate with AI citation frequency across different industries.
How citation patterns change over time and the factors that influence AI models to reference newer vs older content.
Examining how Google's Gemini and Anthropic's Claude approach source attribution compared to established providers.
How AI models cite B2B vs B2C websites differently and implications for content strategy optimization.
ChatGPT, Perplexity, Gemini, Claude, and emerging providers across comprehensive testing scenarios
Large-scale analysis across multiple industries, query types, and content formats for statistical significance
AI models show preference for high-authority sources, with 60% of citations coming from top-10 ranked domains
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.
Standardized queries across multiple AI providers with consistent parameters to ensure comparable results
Advanced text analysis and citation pattern detection with confidence intervals and significance testing
Manual review of automated findings by domain experts to ensure accuracy and contextual understanding
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