Zipf's Law: What This Metric Means in Text Analysis
Zipf's Law is an empirical observation: in natural text, the frequency of a word is inversely proportional to its rank in frequency. The most frequent word occurs about twice as often as the second, three times as often as the third, and so on.
Where It Came From
The pattern was described by linguist George Zipf in the first half of the twentieth century, and later it was found far beyond language — in city sizes, incomes, website traffic. For texts, it holds surprisingly stable: in any sufficiently large natural text, the frequency distribution fits the same curve.
Why It's in SEO Tools
Deviation from the curve is an indirect sign that the text was written in a somewhat unusual way: keyword stuffing, assembled from templates, or generated. Therefore, the metric is used in text analysis: in our AI text detector, it is one of thirteen independent metrics and is responsible for the distribution of word frequencies.
Practical Benefits for Texts
- Spam Diagnosis: if a keyword deviates from the distribution, it is repeated too often — this is also visible in density analysis.
- Assessment of Vocabulary Richness: a flat distribution indicates a poor language.
- Comparison with Competitors: more useful than abstract norms — see how top texts look.
A Simple Example
Take any large text and count the word frequencies. The top positions will almost always be occupied by function words — prepositions, conjunctions, pronouns — and their frequencies will decrease along a predictable curve. Significant words of the topic will be lower, and their share shows what the text is about. If a thematic word suddenly appears at the very top next to prepositions, that is spam, visible without any tools.
What the Law Does Not Do
It does not directly influence ranking and is not a factor. It is a measuring tool, not a rule to conform to: trying to fit the text to the curve is pointless.