This Project Aims to “De-Flatten” Digital Publishing by Matching the Best Content With Premium Ads
Can an algorithm sift through piles of content to deliver top advertising dollars to top quality journalism?
The News Quality Scoring Project is relying on some humans and, well, an algorithm to separate “commodity news” from “value-added news” in an attempt to connect high quality content with advertisers who want better exposure.
It’s a tricky topic, but one that Frederic Filloux (the founder of digital media business newsletter Monday Note, a 2017 Knight Fellow, and now a Knight Senior Research Fellow) is attempting to tackle through the NQS project. Filloux is designing a mechanism to assess news articles based on quantifiable and qualitative signals — like word count, freshness versus evergreen material, quote density, contextualization, and the presence of a byline — in order to surface higher quality content for both readers and publishers.
“The digital publishing system has the big inconvenience of flattening everything,” Filloux said. It’s much more expensive to produce a six-month, heavily fact-checked investigation than “a piece of news put together by a couple of interns” — but the return on investment in terms of advertising funds is the same per click. His system, however, assigns each piece of news a score based on the value-add (determined by the news organization) and the previously mentioned signals; these signals can then be detected by an ad server to serve “pricier, premium advertising.”