Using Big Data, AI Can Predict the Next Bestseller

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Less than one-half of one percent of all aspiring authors reach the best-seller list. Publishers receive thousands of manuscripts a year and even the literary titan like T.S. Eliot erred when he rejected the now-famous novel “Animal Farm” by George Orwell.

Now, Two scientists, Jodie Archer and Matthew Jockers claim to have created a new artificial intelligence (AI) algorithm, called the “bestseller-ometer”, that uses Big Data to predict the next best-seller with more than 80% accuracy. For a struggling publishing industry where the economic stakes couldn’t be higher for selecting the next winning novel, such a quantitative tool may be a windfall.

In their new book, “The Bestseller Code: Anatomy of the Blockbuster Novel,” from St. Martin’s Press, these researchers from the Stanford Literary Lab, describe how they analyzed over 25,000 novels and determined some 2799 relevant features for training their algorithms to detect bestsellers. Despite excitement by the predictive success of their algorithm, it is not without its critics. Skeptics argue that such a code may not detect truly new ideas and would create a stale “literary painting by numbers”.

Literary DNA: Theme and Plotline

Despite criticism, Archer and Jockers work is one of many that are attempting to quantify the humanities. Their …

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