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

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.