---
language: en
title: "Co-occurrence"
description: "Co-occurrence identifies terms that appear together in a text unit. Learn how to choose the scope and investigate brand associations without inferring authority."
canonical: "https://tropk.ai/en/resources/glossary/co-occurrence"
last_modified: "2026-10-12T16:12:29.788Z"
---

# Co-occurrence

Reviewed on 2026-10-06.

## What it means

Co-occurrence is the joint presence of two or more terms within a predefined text unit, such as a sentence, paragraph, or document. In brand analysis, the pair may consist of a company name and an attribute. Frequency depends both on the material analyzed and on the distance or scope used to determine whether terms appear together.

## Why it matters for marketing

For marketing, this observation makes it possible to examine which topics accompany a brand in available content. It helps detect inaccurate descriptions or differences between planned and published messages. However, counting nearby words is not enough to interpret the relationship: a sentence may affirm, deny, or compare an attribute, producing the same joint presence with very different meanings.

## Example in practice

Imagine a packaging company reviewing twenty distributor descriptions. In six paragraphs, its name appears alongside biodegradable, even though the product described is only recyclable. The team rereads the six passages, identifies four inaccurate claims and two explicit negations. It requests corrections only to the claims, preserving the negations that accurately describe the material's limitations.

## How to apply or verify

Define the document set, time period, name spellings, and counting unit. Record the passages, not just the total, and account for republications to avoid artificial duplication. If you switch from paragraphs to pages, recalculate the results. Human review should check for negations, homonyms, and indirect relationships before attributing a topic to the brand.

## Important distinctions

Co-occurrence does not require a link or source [citation](/en/resources/glossary/ai-citation) and is not equivalent to [co-citation](/en/resources/glossary/co-citation). GloVe uses statistics about words that appear together in a corpus to learn vector representations, but this does not allow you to claim how a specific assistant learned a particular association. Repeating a brand and attribute on pages does not prove authority, the accuracy of the attribute, or a future recommendation by AI.

## Sources and references

- [The AI Search Glossary: A definition of 300+ terms](<https://www.tryprofound.com/blog/ai-search-glossary>) — Profound
- [GloVe: Global Vectors for Word Representation](<https://nlp.stanford.edu/projects/glove/>) — Stanford NLP

## Continue a leitura

- [Brand association in AI](</en/resources/glossary/brand-association-in-ai>)
- [Co-citation](</en/resources/glossary/co-citation>)
- [Embeddings](</en/resources/glossary/embeddings>)

## About the author

[Luiz Gardelin](</en/team/luiz-gardelin>)

Luiz Gardelin is the founder of Tropk.ai and works in GEO/AEO, product vision, strategy, and growth, helping brands understand and improve their presence in AI responses.

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