A practical framework for separating whether a source is cited, how crowded the response is, how often it recurs, and where its first attribution appears.
Citation Rate measures occurrence
Citation Rate is the share of eligible observations that explicitly cite a source, domain, or brand-owned property. The level of analysis must be stated: a page-level rate and a brand-network rate are not interchangeable.
Citation Breadth measures the response, not the source
Breadth counts distinct citations or domains in one answer. A response with ten sources offers a different visibility environment from one with two, but more sources do not mean that each source contributed more deeply.
Frequency and share answer competitive source questions
Source Frequency counts how often a domain appears across citations. Citation Share divides a source’s citations by all qualified citations in scope. These measures expose repeated source preference and concentration across engines or themes.
First citation position adds prominence context
The ordinal location of a source’s first citation can be useful when interfaces present numbered sources or ordered reference panels. The extraction rule must be consistent because inline links, footnotes, and source cards create different position semantics.
Citation occurrence, citation competition, and citation position are three separate signals.
Questions about this topic
What is the correct denominator for Citation Rate?+
It depends on the research question, but it must explicitly identify whether eligibility means all valid responses, search-enabled responses, or retrieved observations.
Is Citation Breadth a source success metric?+
Not by itself. It describes how many sources the response exposes and can dilute or diversify the citation environment.
What is Citation Share?+
A source or domain’s portion of all qualified citations within a defined prompt, engine, market, and time scope.
Does first citation position prove influence?+
No. It is a prominence signal; absorption or claim-level analysis is needed to estimate influence.
References
Sources are listed in APA 7 style. Preprints are identified as such and should not be treated as peer-reviewed findings unless separately published.
- Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., & Narasimhan, K. (2024). GEO: Generative engine optimization. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 5–16). Association for Computing Machinery. https://doi.org/10.1145/3637528.3671900
- Martinez, O. (2026). Optimizing visibility in generative engines: A critical survey of generative engine optimization (2023–2026) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2607.14035
- Zhang, K., He, X., & Yao, J. (2026). From citation selection to citation absorption: A measurement framework for generative engine optimization across AI search platforms [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2604.25707