Public SPHERE SEARCH

Cortico’s first product, Earshot, enables ear-to-the-ground listening to better understand our communities — and by extension the nation — through localized search of public conversation across many sources.

Imagine if you could search what people are talking about anywhere in the US with just a few keystrokes:

  • What are people living near the US-Mexico border saying about building the wall?

  • What do people in the rust belt think about China tariffs?

  • How is #metoo being discussed by women in rural Wisconsin?

If you’re a journalist looking to capture the essence of community life — or for that matter a pollster or a curious citizen looking to understand opinion on the ground — you can’t answer these questions with Google, Twitter, or any other platform. This is because some of the richest sources of insight (such as local voices) are buried in communities; some (such as social media) are difficult to anchor locally; and others (such as local talk radio) are hiding in plain sight but not machine-readable.

To power Earshot and future applications, we are building media analytics technology that leverages state-of-the-art machine learning algorithms developed at LSM and elsewhere to capture and organize relevant discourse from diverse public sources, including social media, talk radio, digital and TV news, and community conversations (see Local Voices Network below). For example, this system will soon have the capability to ingest and automatically transcribe speech 24/7 from more than a thousand talk radio stations in the U.S.   

Our technology automatically links all of these conversations across sources, locates them geographically and measures their health. The result is a comprehensive, searchable, high-fidelity record of what people are talking about community-by-community across the nation.

Cortico plans to launch a beta version of Earshot search for journalists in Q4 2018 with data from Twitter, local talk radio, and community conversations. Apply to join our beta program!