# Podcast Mentions

**Overview**\
Podcast Mentions track how often a specific keyword is spoken or referenced across a large corpus of podcasts. In TickerTrends, this data reflects cultural conversation, expert commentary, and early narrative momentum among creators, analysts, and industry insiders. Because podcasts often discuss trends before they hit mainstream media, this dataset provides a forward looking signal of attention and interest.

**Historical Length**\
Coverage varies but is typically 3+ years of historical daily length. 

**Granularity**\
Daily data is the default. In TickerTrends, we aggregate exact match keyword references across the full podcast corpus and structure the results as a continuous time series that supports both short horizon and long horizon analysis.

**Update Frequency**\
Data is updated weekly. Users usually see a one to two week lag between our podcast corpus source and the keyword mentions appearing inside TickerTrends.

**Methodology**\
We scan a large and diverse podcast corpus for exact phrase matches indicated by quotation marks. Only precise matches of the term are counted, which ensures clean measurement of true keyword usage rather than partial or contextual noise. These counts are then aggregated by day to show how often a topic is directly referenced across the podcast ecosystem.

**Example Visualization**

<figure><img src="/files/AzE8ClZLFzstlLrKS4lE" alt=""><figcaption></figcaption></figure>

**Use Cases**

* Identify rising narrative momentum before it appears in news or social media.
* Monitor expert and industry commentary on new technologies, products, and companies.
* Track long term discussion cycles for AI, fintech, consumer brands, and macro themes.
* Confirm whether spikes in search interest or news attention are being reinforced through podcast conversations.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.tickertrends.io/tickertrends/documentation-v2/2.-data-coverage-and-data-types/2.2-data-types/podcast-mentions.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.