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.

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