The Gawker effect: Can deep learning go deep enough to write tomorrow’s headlines? | #CDSUSA

Editor’s Note: Recently, at the Chief Data Scientist, USA, we had the pleasure of being joined by @SiliconANGLE Media, Inc. (theCUBE) to interview some of our attendees about the world of Data Science. Watch the video below. This article written by R. Danes was originally posted on SiliconANGLE blog.

When now-defunct Gawker revealed its use of analytics in content decisions, many old media types shook their heads; algorithms must not replace human judgement in journalism, they warned. But some believe a happy medium is possible: Data can be sourced and analyzed to inform content writers while leaving them with the final say on what readers see.

Haile Owusu, chief data scientist at Mashable, said that this space where data meets human knowledge workers is fertile ground for innovation. He told Jeff Frick (@JeffFrick), host of theCUBE, from the SiliconANGLE Media team, during the recent Chief Data Scientist, USA event that data practitioners do their best work in tandem with “people who are not especially quantitative, who are expecting — and rightfully so — expecting to extract real, concrete, revenue based value, but are completely in the dark about the details.”

Digital research assistant

Owusu explained how Mashable assists writers with data without encroaching on their judgement. They utilize an accumulated history of viral hits, its Velocity Technology Suite and its CMS.

“What we found is that writers are able to distill from sort of a collection of greatest hits — filtered by topic, filtered by time window, filtered by language key words — they are able to incorporate that collected history into their writing,” he said, adding that it does not simply fetch more clicks, but actually improves the quality and depth of their writing.

Two heads are better than one

Owusu stated that deep learning neural networks are able to grok the nuances of data in an almost human manner.

“They’ve allowed us to do feature extraction on images and text in a way that we hadn’t been able to before, and there has been a significant improvement in our ability to do predictions along these lines,” he concluded.

Watch the complete video interview below:


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