The infernal machine
The tragedy of modern journalism is that so much effort often goes to waste. All writers know that all-too-often the story they have slaved over, crafted, cared for and meticulously researched will end up being one of the least read on the site. The pain of seeing exactly how many people preferred to read today’s showbiz news rather than your Pulitzer-worthy in-depth reporting is all-too-real.
The only thing worse than not having those special stories read is not getting the chance to write them in the first place. A machine which could predict which stories are going to be read and therefore get resources thrown at them (and removed from others) would be a diabolical thing, right? And yet that’s just what Finnish researchers have gone and done.
It might have some uses, mind.
We found a total of 15 possible use cases to optimise decision-making with the help of modern predictive machine-learning. After careful prioritising with the media company’s project leads, the case selected for testing was this: Is it possible to predict the contribution of a single news story to the willingness of a user to become a subscriber, or stay as a subscriber – before the story is published?
The value of the prediction is obvious: If you get feedback before publishing, you still have time to do something with the story: make a better storyline, headline, or reconsider whether it’s worth publishing after all.