{"id":30,"date":"2017-10-19T15:35:31","date_gmt":"2017-10-19T15:35:31","guid":{"rendered":"http:\/\/research.unl.edu\/annualreport\/2017\/?p=30"},"modified":"2017-11-01T15:41:39","modified_gmt":"2017-11-01T15:41:39","slug":"tool-identifies-traits-of-literary-blockbusters","status":"publish","type":"post","link":"https:\/\/research.unl.edu\/annualreport\/2017\/tool-identifies-traits-of-literary-blockbusters\/","title":{"rendered":"Tool Identifies Traits of Literary Blockbusters"},"content":{"rendered":"
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Matt Jockers<\/figcaption><\/figure>\n

\u201cThere are three rules for writing a novel. Unfortunately, no one knows\u00a0<\/em>what they are.\u201d<\/em>
\n\u2014 W. Somerset Maugham, novelist and playwright<\/p>\n

The popular early 20th-century novelist made this remark long before anyone could imagine programming a room-sized computer to \u201cread\u201d thousands of books and analyze their traits using artificial intelligence.<\/p>\n

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But that\u2019s just what Nebraska researcher Matt Jockers pulled off with collaborator Jodie Archer, a former acquisitions editor for Penguin Books UK. Their work, a blend of computing and the humanities, produced a predictive algorithm capable of forecasting a manuscript\u2019s potential to hit the best-seller list. The research is summarized in The Bestseller Code<\/em>, a book that sheds light on key ingredients of literary blockbusters.<\/p>\n

\u201cThe primary distinguishing feature is human closeness,\u201d said Jockers Susan J. Rosowski Associate Professor of English and associate dean for research and partnerships in the College of Arts and Sciences.<\/p>\n

\u201cCloseness\u201d refers not to sexual intimacy, he said, but to the everyday, meaningful interactions between people. Other recurring traits are colloquialisms, active and determined characters, and plots with a steady rhythm of highs and lows.<\/p>\n

To develop the algorithm, the researchers harnessed the power of Tusker, a supercomputer at the university\u2019s Holland Computing Center, to analyze 4,800 novels published in the past 30 years, including smash hits like Fifty Shades of Grey<\/em>, The Da Vinci Code<\/em> and Gone Girl<\/em>. Using this data, they identified roughly 2,800 characteristics that a computer can use to differentiate between novels that sink or swim to the top.<\/p>\n

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Matt Jockers and Jodie Archer<\/figcaption><\/figure>\n

From this, the \u201cbest-seller-ometer\u201d was born. The algorithm scores books according to their likelihood of becoming a New York Times best-seller. In tests, the model correctly categorized novels 80 percent of the time.<\/p>\n

The algorithm could shake up the publishing industry, enabling authors to examine manuscripts for missing features and publishers to rate promising books.<\/p>\n

But Jockers and Archer agree the tool cannot substitute for skill, creativity and instinct \u2013 prerequisites for superb writing. The aim is not for a computer to script a best-seller, but to develop a valuable tool for writers, readers and publishers.<\/p>\n

They recently formed a consulting company, Archer Jockers LLC, and are working with NUtech Ventures, Nebraska\u2019s technology commercialization affiliate, to license the technology.<\/p>\n

\u201cWe\u2019re keen to see the algorithm leveraged to help writers find a market and to help readers find new books,\u201d Jockers said.<\/p>\n