Fact Density Measurement

Fact density is an objective metric: the number of verifiable, specific claims per 1,000 words. Not "content quality" -that's subjective. We're counting statements containing specific, checkable data points: named statistics with numerical values, dated events, attributed quotations, specific measurements, named entities with factual assertions, and citation references to external sources.

We distinguish between high-value and low-value facts. High-value: "Customer satisfaction increased by 23% after implementing live chat" or "The protocol was ratified in March 2024 by 47 member states." Low-value: "many companies have seen improvements" or "experts agree this is important." Only high-value facts count.

The measurement runs per page, then aggregates across the site. Site-level metrics include: mean fact density across all content pages, median (to resist skew from outlier pages), percentage below the minimum threshold (2 facts per 1,000 words), and percentage above the high-performance threshold (5 per 1,000).

Here's why this matters: AI answer engines are citation machines. They need specific facts to cite. A page full of general advice gives the AI nothing quotable. A page dense with named statistics, specific numbers, and dated references gives the AI multiple citation-worthy fragments to extract. We've seen this pattern repeat across every audit we've run.

AI citation behavior follows a power law -a small number of high-fact-density pages generate the majority of citations. But the distribution across your site affects domain-level authority. When AI engines observe that a domain consistently produces content with high fact density, they develop a trust bias toward that domain for factual queries -even on pages they haven't crawled yet.

The flip side is brutal. A site where 90% of content is generic and fact-sparse trains AI engines to expect low-value content from that domain. Even if you publish one exceptional, data-rich article, the domain's reputation for thin content drags it down. The AI ranks it below a competitor whose entire catalog maintains higher density.

The competitive dynamics are straightforward. Two articles covering the same topic. One has 8 specific statistics with sources. The other has general advice without numbers. AI engines cite the data-rich article. At the site level, a domain where every article has 3-5 specific facts per 1,000 words creates a systematic advantage over competitors publishing fluffy, fact-sparse content.

This is especially critical for YMYL topics -healthcare (solace-health, patientadvocate-org), finance, legal -where AI engines apply heightened scrutiny to factual claims. For these topics, fact density combined with proper source attribution is often the deciding factor in whether an AI engine cites you or skips you entirely.

The audit uses NLP to identify and count factual claims on each content page. Three stages: text extraction, fact detection, fact classification.

In text extraction, we strip HTML to get the body text, excluding navigation, footer, sidebar, and boilerplate. We use the <main> or <article> element as the content boundary when available, falling back to heuristic extraction for pages without semantic HTML. Word count on extracted text establishes the denominator.

In fact detection, pattern matching and NLP heuristics identify potential claims. The patterns: numerical values with context (percentages, dollar amounts, counts, measurements), date references (specific years, months, named time periods), named entity assertions (claims about specific people, companies, organizations), comparative claims with endpoints ("X is 3x faster than Y"), and source attributions ("according to [source]," "a [year] study by [org] found").

In fact classification, each detected claim is labeled high-value or low-value. High-value facts are specific and verifiable -enough detail that a reader could check them. Low-value facts are vague generalizations ("many studies show," "most experts agree") lacking the specificity needed for citation. Only high-value facts hit the final score.

The output: per-page density scores plus a site-wide distribution. Pages are bucketed into four tiers -data-rich (above 5 per 1,000 words), adequate (3-5), thin (1-3), and empty (below 1). A healthy site has fewer than 20% of pages in "thin" or "empty."

Fact density scoring combines per-page and site-wide metrics:

Site-wide distribution (6 points): - 80%+ of content pages have density above 3 per 1,000 words: 6/6 points - 60-79% above 3: 5/6 points - 40-59% above 3: 4/6 points - 20-39% above 3: 2/6 points - Below 20% above 3: 1/6 points - No pages with detectable factual claims: 0/6 points

High-density page percentage (2 points): - 30%+ of content pages exceed 5 per 1,000 words: 2/2 points - 15-29%: 1.5/2 points - 5-14%: 1/2 points - Below 5%: 0/2 points

Fact attribution quality (2 points): - 50%+ of facts include source attribution (named study, linked reference, quoted expert): 2/2 points - 25-49% attributed: 1.5/2 points - 10-24% attributed: 1/2 points - Below 10% attributed (facts stated without sources): 0.5/2 points - No facts detected: 0/2 points

Deductions: - -1 point if more than 20% of claims are recycled from other pages on the same site (internal duplication) - -0.5 points if fact-dense pages lack Article schema with datePublished (facts without dates lose credibility)

Maximum: 10. Content-marketing sites with rigorous editorial standards typically score 6-8. Sites publishing primarily opinion without data typically score 1-3.