Can a computer identify a bestselling book?
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This week's question: Can computers predict a bestseller?
They're sure going to try.
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A company Inkitt bills itself as a "data-driven publisher." How does that work?
At inkitt.com, writers can post their original works and receive feedback from readers. Readers have free access to thousands of those new pieces across myriad genres. But how they read those works is being watched: Inkitt's algorithms reportedly track readers' behavior patterns.
Using those patterns, Inkitt claims it can identify potential hits and bestsellers. ("Read and fall in love with novels before they go mainstream," the site promises.) Inkitt then partners with traditional publishing houses to release the books. The first book identified in this manner will be Erin Swan's "Bright Star," a young adult novel scheduled to be published by Tor Books next summer, according to Digital Publishing News.
This method of using a test group of readers, pulled from the public at large, to weed through manuscripts isn't new. Amazon's Kindle Scout program works similarly. Inkitt's emphasis on data analysis and algorithms, however, is what sets it apart — they're not just crowd-sourcing the sorting of the slush pile, they're analyzing how the crowd does it.
Snapping up what's popular on the internet isn't new either, of course: Self-published hits that find an audience are often later picked up by traditional publishers, after they meet a measure of success.
Two authors with books on the Nielsen BookScan bestseller list last year started by posting their work on the internet. E.L. James, of the "Fifty Shades" empire, originally shared her work on a fan fiction site; and Andy Weir self-published "The Martian" before it became an award-winning film.
Inkitt's model aims to entice such writers to post their work on its site, and then analyze readers' behavior to find the hits. Will it work?
"Bright Star" comes out next summer — we'll see if it's a hit.