Matthew L. Jockers is Associate Dean for Research and Global Engagement in the College of Arts and Sciences and Susan J. Rosowski Associate Professor of English at the University of Nebraska. He is a Faculty Fellow in the Center for Digital Research in the Humanities, Faculty Fellow in the Center for Great Plains Studies, and Director of the Nebraska Literary Lab. Jockers’s research is focused on computational approaches to the study of literature. His books include Macroanalysis: Digital Methods and Literary History (University of Illinois, 2013) and Text Analysis Using R for Students of Literature (Springer, 2014). For more information, see www.matthewjockers.net

The Bestseller Code

The Bestseller Code

Ask most people about massive success in the world of fiction, and you’ll typically hear that it’s a game of hazy crystal balls. The sales figures of E. L. James or Dan Brown seem to be freakish―random occurrences in an unknowable market. But what if there were an algorithm that could reveal a secret DNA of bestsellers, regardless of their genre? What if it knew, just from analyzing the words alone, not just why genre writers like John Grisham and Danielle Steel belong on the lists, but also that authors such as Junot Diaz, Jodi Picoult, and Donna Tartt had telltale signs of success all over their pages?

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