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The bestseller code: anatomy of the blockbuster novel
(Book)

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Contributors:
Published:
New York : St. Martin's Press, 2016.
Physical Desc:
242 pages : illustrations ; 22 cm
Status:
Carmichael
028.9 A671 2016
Central
028.9 A671 2016
North Natomas has 2
028.9 A671 2016
Description

"What if there was an algorithm that could predict which novels become mega-bestsellers? Are books like Dan Brown's The Da Vinci Code and Gillian Flynn's Gone Girl the Gladwellian outliers of publishing? The Bestseller Code boldly claims that the New York Times bestsellers in fiction are predictable and that it's possible to know with 97% certainty if a manuscript is likely to hit number one on the list as opposed to numbers two through fifteen. The algorithm does exist; the code has been cracked; the results are in; and they are stunning. The system analyzes themes, plot, character, pacing, even the frequency of words and punctuation, to predict which stories will resonate with readers. A 28-year-old heroine is a big plus. So is realism. Giving 30% of your novel to only two specific topics. And if you can include a dog rather than a cat and few sex scenes, you have a better chance of writing a bestselling novel. The project is an investigation into our intellectual and emotional responses as humans and readers to books of all genres. It is a big idea book that will appeal to fans of The Black Swan by Nassim Taleb, a book for data-mining nerds, as well as a book about writing, reading, and publishing. Anyone who has ever wondered why Gone Girl, Girl on the Train or The Girl With the Dragon Tattoo captured so many readers worldwide will find their interest piqued"--

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028.9 A671 2016
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Format:
Book
Edition:
First edition.
Language:
English
ISBN:
9781250088277

Notes

Description
"What if there was an algorithm that could predict which novels become mega-bestsellers? Are books like Dan Brown's The Da Vinci Code and Gillian Flynn's Gone Girl the Gladwellian outliers of publishing? The Bestseller Code boldly claims that the New York Times bestsellers in fiction are predictable and that it's possible to know with 97% certainty if a manuscript is likely to hit number one on the list as opposed to numbers two through fifteen. The algorithm does exist; the code has been cracked; the results are in; and they are stunning. The system analyzes themes, plot, character, pacing, even the frequency of words and punctuation, to predict which stories will resonate with readers. A 28-year-old heroine is a big plus. So is realism. Giving 30% of your novel to only two specific topics. And if you can include a dog rather than a cat and few sex scenes, you have a better chance of writing a bestselling novel. The project is an investigation into our intellectual and emotional responses as humans and readers to books of all genres. It is a big idea book that will appeal to fans of The Black Swan by Nassim Taleb, a book for data-mining nerds, as well as a book about writing, reading, and publishing. Anyone who has ever wondered why Gone Girl, Girl on the Train or The Girl With the Dragon Tattoo captured so many readers worldwide will find their interest piqued"--,Provided by publisher.
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Citations
APA Citation (style guide)

Archer, J., & Jockers, M. L. (2016). The bestseller code: anatomy of the blockbuster novel. First edition. New York, St. Martin's Press.

Chicago / Turabian - Author Date Citation (style guide)

Archer, Jodie and Matthew Lee Jockers. 2016. The Bestseller Code: Anatomy of the Blockbuster Novel. New York, St. Martin's Press.

Chicago / Turabian - Humanities Citation (style guide)

Archer, Jodie and Matthew Lee Jockers, The Bestseller Code: Anatomy of the Blockbuster Novel. New York, St. Martin's Press, 2016.

MLA Citation (style guide)

Archer, Jodie. and Matthew Lee Jockers. The Bestseller Code: Anatomy of the Blockbuster Novel. First edition. New York, St. Martin's Press, 2016.

Note! Citation formats are based on standards as of July 2022. Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy.
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Grouped Work ID:
2adf3679-31a7-3696-0f58-7556a11b3c47
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Record Information

Last Sierra Extract TimeApr 15, 2024 05:10:58 PM
Last File Modification TimeApr 15, 2024 05:11:32 PM
Last Grouped Work Modification TimeApr 20, 2024 02:11:00 AM

MARC Record

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