"When a story captures the imagination of millions, that's magic. Can you qualify magic? Archer and Jockers just may have done so."—Sylvia Day, New York Times bestselling author 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? Thanks to Jodie Archer and Matthew Jockers, the algorithm exists, the code has been cracked, and the results bring fresh new insights into how fiction works and why we read. The Bestseller Code offers a new theory for why Fifty Shades of Grey sold so well. It sheds light on the current craze for dark heroines. It reveals which themes tend to sell best. And all with fascinating supporting data taken from a five-year study of twenty thousand novels. Then there is the hunt for "the one"—the paradigmatic example of bestselling writing according to a computer's analysis of thousands of points of data. The result is surprising, a bit ironic, and delightfully unorthodox. This book explains groundbreaking text-mining research in accessible terms and offers a new perspective on the New York Times bestseller list. It's a big-idea book about the relationship between creativity and technology that will be provocative to anyone interested in how analytics have already transformed the worlds of finance, medicine, and sports. But at heart it is a celebration of books for readers and writers—a compelling investigation into how successful writing works, and a fresh take on our intellectual and emotional response to stories.

What if an algorithm could predict which manuscripts would become mega-bestsellers? 'Girl on the Train'. 'Fifty Shades'. 'The Goldfinch'. Why do some books capture the whole world's attention? What secret DNA do they share? In 'The Bestseller Code', Archer and Jockers boldly claim that blockbuster hits are highly predictable, and they have created the algorithm to prove it. Using cutting-edge text mining techniques, they have developed a model that analyses theme, plot, style and character to explain why some books resonate more than others with readers.

This sneak peek teaser - featuring literary giants John Grisham and Danielle Steele - from Chapter 2 of The Bestseller Code, a groundbreaking book about what a computer algorithm can teach us about blockbuster books, stories, and reading, reveals the importance of topic and theme in bestselling fiction according to percentages assigned by what the authors refer to as the “bestseller-ometer.” Although 55,000 novels are published every year, only about 200 hit the lists, a commercial success rate of less than half a percent. When the computer was asked to “blindly” select the most likely bestsellers out of 5,000 books published over the past thirty years based only on theme, it discovered two possible candidates: The Accident by Danielle Steel and The Associate by John Grisham. The computer recognized quantifiable patterns in their seemingly opposite, but undeniably successful writing careers with legal thrillers and romance. In Chapter 2, Archer and Jockers analyze this data and divulge the most and least likely to best sell topics and themes in fiction with a human discussion of the “why” behind these results. The Bestseller Code is a big-idea book about the relationship between creativity and technology. At heart it is a celebration of books for readers and writers—a compelling investigation into how successful writing works.

While the term 'bestseller' explicitly relates books to sales, commercially successful books are also products of individual creative work. This Element presents a new perspective on the relationship between art and the market, with particular reference to bestselling writers and books. We examine some existing perspectives on art's relationship to the marketplace to trouble persistent binaries that see the two in opposition; we break down the monolith of the marketplace by thinking of it as made up of a range of invested, non-hostile participants such as publishing personnel and readers; we articulate the material dimensions of creative writing in the industry through the words of bestselling writers themselves; and we examine how the existence of bestselling books and writers in the world of letters bears enormous influence on the industry, and on the practice of other writers.

Mass-Market Fiction and the Crisis of American Liberalism, 1972–2017 tracks the transformation of liberal thought in the contemporary United States through the unique lens of the popular paperback. The book focuses on cultural shifts as they appear in works written by some of the most widely-read authors of the last fifty years: the idea of love within a New Economy (Danielle Steel), the role of government in scientific inquiry (Michael Crichton), entangled political alliances and legacies in the aftermath of the 1960s (Tom Clancy), the restructured corporation (John Grisham), and the blurred line between state and personal empowerment (Dean Koontz). To address the current crisis, this book examines how the changed character of American liberalism has been rendered legible for a mass audience.

Can established humanities methods coexist with computational thinking? It is one of the major questions in humanities research today, as scholars increasingly adopt sophisticated data science for their work. James E. Dobson explores the opportunities and complications faced by humanists in this new era. Though the study and interpretation of texts alongside sophisticated computational tools can serve scholarship, these methods cannot replace existing frameworks. As Dobson shows, ideas of scientific validity cannot easily nor should be adapted for humanities research because digital humanities, unlike science, lack a leading-edge horizon charting the frontiers of inquiry. Instead, the methods of digital humanities require a constant rereading. At the same time, suspicious and critical readings of digital methodologies make it unwise for scholars to defer to computational methods. Humanists must examine the tools--including the assumptions that went into the codes and algorithms--and questions surrounding their own use of digital technology in research. Insightful and forward thinking, Critical Digital Humanities lays out a new path of humanistic inquiry that merges critical theory and computational science.