Web design · Development · SEO
WDF
WDF explained: term weighting within a document
Editorially reviewed ·
Clear definition
WDF stands for Within Document Frequency and describes a weighted frequency of a term within one document. Unlike simple keyword density, WDF formulas may reduce the influence of excessive repetition logarithmically. The exact formula is not universally standardised and varies between analysis tools.
WDF can highlight unusual term distributions when comparing texts, but it is not a known Google ranking formula. Search engines assess language, meaning, context, quality, and many other signals far more broadly. Content should not be written to reach an assumed ideal WDF value; completeness, clarity, and search intent matter more.
At a glance
WDF in practice
WDF describes the relative frequency of a term within a document. It can support text comparison but says nothing by itself about accuracy, readability, or suitability for search intent.
SEO tools often compress complex observations into a score. Such values can support comparison and prioritisation, but they do not fully represent search algorithms or user satisfaction. Before acting, it should be clear how a metric is calculated, what evidence is missing, and whether a change genuinely relates to the business objective.
WDF: relevance to SEO, paid search, and GEO
A method is useful when it produces a testable hypothesis. Rather than pursuing a target score blindly, a team should implement a concrete improvement and observe visibility, behaviour, and conversion. Paid-search data can add evidence about queries and messages, but auction, targeting, and budget mean it cannot be transferred directly to organic performance.
For search and answer systems, coverage of WDF should distinguish its definition, scope, and evaluation criteria. The editorial reference is “Term-weighting approaches in automatic text retrieval” by Cornell University, making central claims traceable for readers and machine-based systems.
Sources and further reading
- Research Term-weighting approaches in automatic text retrieval Cornell University · Checked
