For web publishers, engagement is a valuable metric. In fact, content optimization platform Parse.ly claims audience attention — which it defines as the way topics, moments, contexts, locations, devices, and sources interact with each other in real time — is more predictive of behavior than demographics, social signals, and search queries. Case in point: In a recent study, it found that attention data can accurately predict a movie’s box office success several weeks before the premiere.

That’s why Parse.ly in June launched Currents, a new feature that peels back the curtains on attention and its contributing influences. And it’s why the company is today making Currents available to all customers — including those on its free tier.

“If there’s one thing the media industry needs, it’s transparency. That’s been my personal mission since starting Parse.ly with my cofounder several years ago,” Parse.ly CTO Andrew Montalenti said. “We think that Currents will shine a bright light on how news and content on the internet really works … [it’s] like a live poll of the internet.”

Above: Conducting a topic search within Currents.

Image Credit: Parse.ly

Currents comprises five core data dimensions: Story Clusters, or groupings of closely related articles; Topics; Categories; Traffic Sources; and Geography. A sophisticated machine learning backend enables it to learn news story topics and categories automatically, and by honing in on the “meaningful” words in text — that is to say, those related to people, places, things, and ideas — it’s able to suss out the…

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