GEO-16 Framework for AI Citation Prediction

• GEO-16 is a multidimensional framework that measures on-page quality through 16 distinct pillars to predict AI citation likelihood.
• It utilizes rigorous mathematical scoring and banding methods to derive a normalized score that guides actionable publisher benchmarks.
• Statistical models and engine contrasts validate GEO-16's approach, offering practical strategies to enhance metadata, structure, and citation outcomes.

The GEO-16 framework is a multidimensional, empirically validated approach for auditing and predicting the citation behavior of AI answer engines based on granular on-page quality signals. Designed to quantify and optimize the likelihood that web pages are referenced by leading generative systems—including Brave Summary, Google AI Overviews, and Perplexity—GEO-16 defines sixteen orthogonal pillars, each measured via formal sub-signals and aggregated into both discrete bands and a normalized global score. This framework establishes both rigorous mathematical definitions and practical operating points, yielding actionable benchmarks and a playbook for publishers seeking higher visibility in automated citation environments.

1. Formal Specification: Pillar Measurement and Score Construction

GEO-16 operationalizes page quality through sixteen pillars, each independently capturing a facet of web content observable by automated audits. For page $u$, each pillar $j$ is measured with respect to a set of sub-signals $s_{j,i}(u)\in[0,1]$, reflecting the presence, correctness, or strength of on-page features.

Weighted aggregation yields a raw pillar score: $$S_j(u)\;=\;\sum_{i\in\mathcal I_j}w_{j,i}\;\,s_{j,i}(u)\,,\quad S_j\in[0,1]$$ where weights $w_{j,i}\ge0$ satisfy $\sum w_{j,i}=1$ within each pillar.

Pillar scores are mapped to integer bands: $$b_j(u)\;\in\;\{0,1,2,3\},\quad \text{with}\;\; b_j=0\text{~if~}S_j<0.25,\; b_j=1\text{~if~}0.25\le S_j<0.50,\; b_j=2\text{~if~}0.50\le S_j<0.75,\; b_j=3\text{~if~}S_j\ge0.75$$

A "pillar hit” is defined as: $h_j(u)\;=\;\mathbf1\{\,b_j(u)\ge2\}$ and the total pillar hit count is: $H(u)\;=\;\sum_{j=1}^{16}h_j(u)\in\{0,1,\dots,16\}$

The normalized GEO score is: $$G(u)\;=\;\frac{1}{48}\sum_{j=1}^{16}b_j(u)\;\in[0,1]$$ ensuring rigorous scaling such that $G=1$ only if all $b_j=3$.

GEO-16 Pillars and Key Sub-signals

Pillar	Key Signals	Typical Source
Metadata & Freshness	JSON-LD dates, visible timestamps, sitemaps	Structured data, markup
Semantic HTML	<h1> count, heading hierarchy, ARIA roles	HTML, WAI-ARIA labeling
Structured Data	Article/FAQPage schema, required properties	JSON-LD, schema validation tools
UX Readability	Flesch-Kincaid, paragraph length, mobile viewport	Content, meta information
Claims Accuracy	Fact-check icons, disclaimers	Iconography, editorial disclosure
Microcontent	TL;DR, key takeaways, clear headings	Dedicated summaries, headings
Authority & Trust	Outbound .gov/.edu, domain authority	Link analysis, third-party metrics
Evidence & Citations	Inline references, bibliography	citation formatting, references
Transparency & Ethics	Sponsorship/conflicts disclosure, scope statements	disclosures, statements
Content Depth	Word count, headings, further reading	main body, navigation
Internal Linking	Contextual anchor linking, link density	in-site navigation
External Linking	External anchors, link-health	outbound link metadata
Engagement & Interaction	Comments, CTAs, read-progress	UI components
Visuals & Media	Images/videos, alt text, SVG diagrams	embedded media

2. Statistical Modeling: Thresholds and Predictive Performance

The framework treats combinations of $G(u)$ and $H(u)$ as binary classifiers for citation outcomes: $$f_{g,h}(u)\;=\;\mathbf1\{\;G(u)\ge g\;\wedge\;H(u)\ge h\}$$ where $g^*=0.70$, $h^*=12$ are empirically derived operating points optimized via Youden’s index: $J(g,h)=\mathrm{TPR}(g,h)-\mathrm{FPR}(g,h)$ Pages satisfying $G\ge0.70$ and $H\ge12$ achieved a 78% citation rate, sensitivity ≈ 0.78, specificity ≈ 0.84. Using pillar hits alone ($H\ge12$) obtained sensitivity 0.85 and specificity 0.79, indicating that both breadth and overall quality are significant predictors.

3. Logistic Regression: Incremental Effects and Diagnostics

The incremental contribution of GEO dimensions to citation likelihood is estimated by fitting logistic regression models: $\logit[\Pr(Y_e(u)=1)] =\alpha +\beta_G\,G(u) +\beta_H\,H(u) +\sum_{e'\neq\text{Perp}} \beta_{e'}\,\mathbf1\{e=e'\} +\sum_{v\neq\text{ref}} \gamma_v\,\mathbf1\{v(u)=v\}$ using domain-clustered standard errors. The estimated effects are:

• $\beta_G\approx\ln(4.2)$: Each unit increase in $G$ multiplies the odds of citation by 4.2 $[3.1, 5.7]$.
• $\beta_H\approx\ln(1.8)$: Each additional pillar hit multiplies odds by 1.8 $[1.4, 2.3]$.
• Brave vs Perplexity OR = 2.1 $[1.6, 2.8]$, Google AIO vs Perplexity OR ≈ 1.9.
• Vertical (Cloud vs Marketing) OR ≈ 1.9 $[1.3, 2.7]$.

Diagnostics confirm model validity: variance-inflation factors <2, Hosmer–Lemeshow non-significant, ROC AUC ≈0.91, Nagelkerke $R^2=0.743$. This suggests high model fit and parsimony for citation prediction.

4. Cross-Engine and Vertical Contrasts

Despite uniform pillar definitions, substantial contrasts emerge across answer engines:

• Brave Summary: Highest mean $G=0.727$, SD = 0.142, citation rate = 78 %, mean $H=11.6$.
• Google AI Overviews: Mean $G=0.687$, SD = 0.158, citation rate = 72 %, mean $H=11.0$.
• Perplexity: Most permissive (mean $G=0.300$, SD = 0.189, citation rate = 45 %, mean $H=4.8$).

Across verticals, "Cloud" and "Insurance" domains scored higher on average GEO, with "Customer Service" and "HR" trailing. Extended models demonstrate mild engine-specific variation in pillar elasticity, but strong and consistent preference for Metadata & Freshness, Semantic HTML, and Structured Data pillars.

5. Reliability, Limitations, and Threats to Validity

Reliability checks support robustness:

• Inter-rater agreement on pillar bands (Cohen’s $\kappa>0.80$, 5% subset).
• Temporal stability (Pearson $r>0.95$ week-over-week for pillar bands).

Key limitations:

• Observational design with potential unobserved confounders (e.g., backlinks, brand reputation).
• Focus on English-language, B2B SaaS verticals at a single time point; external validity to other languages/sectors untested.
• No experimental manipulation of off-page authority; as such, causal inferences about earned-media effects remain for future work.

A plausible implication is that while GEO-16 norms predict citation within the studied setting, transferability to alternate verticals or languages requires further empirical investigation.

6. Publisher Playbook: Empirically Driven Recommendations

Translating empirical results, the framework recommends four high-impact publisher strategies:

1. Show your date: Prominently display and encode both visible and machine-readable datePublished/dateModified across page content and JSON-LD.
2. Header hygiene: Enforce exactly one <h1>, coherent <h2>/<h3> hierarchy, and appropriate landmark/ARIA roles.
3. Structured data quality: Ensure complete, error-free Article or FAQPage schema implementation with all recommended properties.
4. Broaden strong pillars: Target at least 12 pillars achieving band ≥2 and overall $G\ge0.70$.

Additionally, offset answer engine brand biases by cultivating citations on third-party authoritative domains (earned media).

7. Context and Significance

By integrating granular audits of on-page features with cross-engine citation outcomes, GEO-16 supplies the mathematical mechanisms ($G$, pillar bands), optimized thresholds (78% citation at $G\ge0.70, H\ge12$), and empirically validated strategic guidance necessary for publishers aiming to maximize visibility. The approach stands as both a technical standard and actionable blueprint for competitive citation in the era of AI-powered synthesis and retrieval (Kumar et al., 13 Sep 2025).