> For the complete documentation index, see [llms.txt](https://docs.tickertrends.io/tickertrends/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tickertrends.io/tickertrends/documentation-v2/2.-data-coverage-and-data-types/2.2-raw-data-types/keyword-mentions-in-job-posts.md).

# Keyword Mentions in Job Posts

#### Overview

**Keyword Mentions in Job Posts** track how often a specific skill, tool, or technology appears across job postings over time. In TickerTrends, this dataset captures ongoing hiring demand, showing how widely a keyword is appearing across the job market.

Unlike first mentions, which isolate initial adoption decisions, total keyword mentions reflect the **depth and persistence of demand**. This makes the dataset useful for measuring whether a technology is becoming more embedded in hiring requirements across companies and roles.

#### Historical Length

Coverage varies by dataset, but typically includes **multiple years of historical job posting data**, allowing users to analyze long-term hiring and technology demand trends.

#### Granularity

Daily data is the default. In TickerTrends, we count the number of job postings that mention a specific keyword each day and aggregate those counts into a continuous time series.

This creates a clear view of when demand for a skill or technology is rising, stabilizing, or declining across the hiring market.

#### Update Frequency

Data is typically updated within one week.

#### Methodology

We scan job postings across a large and diverse dataset, tracking keyword mentions within job descriptions and titles. Each time a tracked keyword appears in a qualifying job posting, that posting contributes to the daily count.

Unlike first-mention datasets, repeated appearances across multiple postings are included. This ensures the series reflects **ongoing hiring intensity and sustained employer demand**, not just initial adoption events.

These daily counts are then aggregated into a time series showing how often a technology, tool, or skill is being requested across the labor market over time.

#### Example Visualization

A time series showing the total number of job postings mentioning a technology or skill such as “OpenAI,” “Snowflake,” “Claude,” “MongoDB,” or “Excel.” Spikes indicate periods where hiring demand and employer interest are increasing.

<figure><img src="/files/zq9eWwTyS3sunOcjFGYj" alt=""><figcaption></figcaption></figure>

#### Use Cases

* Measure sustained hiring demand for specific technologies and skills
* Compare ongoing employer interest across competing tools or platforms
* Validate whether early adoption signals are turning into broader workforce demand
* Track acceleration or slowdown in technical hiring requirements
* Support investment research with real labor market behavior