The Bestseller Code

I honestly thought I would enjoy this book more than I did. Part of the problem might have been the not-so-secret snobbishness I have when it comes to bestselling novels. There's a little voice in my head that tells me that if a book appeals to the masses, it's probably not going to do much for me. And, in most cases, that's true. I don't very often read titles that make the lists, and when I do, it's usually by accident, or if the book has been chosen by my book club. I've never read anything by <a href="https://goodreads.com/author/show/630.Dan_Brown" title="Dan Brown" rel="noopener">Dan Brown</a>, <a href="https://goodreads.com/author/show/7128.Jodi_Picoult" title="Jodi Picoult" rel="noopener">Jodi Picoult</a>, or <a href="https://goodreads.com/author/show/3780.James_Patterson" title="James Patterson" rel="noopener">James Patterson</a>. And, no, I've never read <a href="https://goodreads.com/book/show/10818853.Fifty_Shades_of_Grey__Fifty_Shades___1_" title="Fifty Shades of Grey (Fifty Shades, #1) by E.L. James" rel="noopener">Fifty Shades of Grey</a>.<br /> <br />There is some interesting information here, but it does tend to get repetitive. I had the feeling I so often get when reading nonfiction, that the contents could have easily been covered in a magazine article. The facts I found most interesting were that a novel's first sentence is frequently an indicator of its possible financial success, that a computer rightly deduced <a href="https://goodreads.com/author/show/383606.Robert_Galbraith" title="Robert Galbraith" rel="noopener">Robert Galbraith</a> was actually <a href="https://goodreads.com/author/show/1077326.J_K__Rowling" title="J.K. Rowling" rel="noopener">J.K. Rowling</a>, and that out of all the bestselling authors, <a href="https://goodreads.com/author/show/721.John_Grisham" title="John Grisham" rel="noopener">John Grisham</a> and <a href="https://goodreads.com/author/show/14255.Danielle_Steel" title="Danielle Steel" rel="noopener">Danielle Steel</a> hit the right buttons more than any other writers. <br /> <br />It should be noted - there are a lot of spoilers; plots (and endings) of many bestsellers are discussed in great detail. On the plus side, anyone who's fond of charts and graphs should be delighted by this book. Personally, I think the data discovered will appeal more to writers than readers. I much preferred a fiction book I read recently on this same subject - <a href="https://goodreads.com/book/show/6013046.How_I_Became_a_Famous_Novelist" title="How I Became a Famous Novelist by Steve Hely" rel="noopener">How I Became a Famous Novelist</a>. <i>That one</i>, I would recommend.

There's some good advice in here, even if it mostly feels kind of icky. <br /><br />Mostly it's a commercial for the services the authors offer.<br /><br />But I did learn a few things! So that's good!<br /><br />I guess someday if (when???) I get a seven-figure advance and become the new JK Rowling, I'll change this to five stars. Ha.

This book ended up being even more amazing than I expected.<br /><br />The authors are both literary/publishing experts and have worked on machine learning for years. They fed 5,000 books, published over the past 30 years, to their computer programs. 500 of those were NY Times bestsellers and the rest weren't. They had programs that analyzed, for each book, the themes and topics, ups and downs of the plot, characters and the style. They had an in-sample--10% of bestsellers and 10% of non-bestsellers--that was used to train their programs, and then they forecasted how likely the out-of-sample books were going to be bestsellers.<br /><br />They were right about 80% of the time!<br /><br />How did they do it and what are some of the conclusions? I won't spill all the beans, but here are some examples:<br /><br />They analyze topics by looking at nouns that are close to each other. So if "beer" and "coctail" are near "bar", the computer concludes the book is talking about a bar in which people drink rather than a bar exam taken by lawyers or a bar used to do pull-ups. (You can see the complexity here--the computers have to get the meaning like people--from contex--in order to learn to read, but that's only the first step.)<br /><br />Then they looked at hundreds of topics such as guns or health emergency or sex across their sample of books to see which topics were used by the bestsellers and which ones weren't. The same for non-bestsellers. They noticed that sex doesn't sell, for instance. <br /><br />In addition, the number of topics and how often a topic appeared were even more important i.e. a book shouldn't try to cover too many topics. <br /><br />How about the plot? They looked at words that showed character feelings to determine whether good or bad/dangerous things were happening to the characters. The cumulative effect is a curve that shows ups and downs of the plot--the emotional plotline. You want to see curves, of course. Two winners in this chategory were the two best selling adult books in the last thirty years: the Da Vinci Code and the Fifty Shades of Grey. Cool!<br /><br />The next big thing is characters. To figure them out, you want to see what they say, think and do. The authors accomplished that by analyzing verbs. The conclusion is that active characters are better than passive--no surprise there! Verbs like "need" and "want" are much better than "wish". <br /><br />Lastly, there is no good book without a good writing style. Even fewer surprises there. Basically, what the books on writing and editing teach truly works: use contemporary language, contractions like wouldn't, shorter sentences etc. <br /><br />I have listed a few examples here. There's a lot more in the book. <br /><br />One interesting thing is their models told them fantasy and science fiction don't work. People like to be in our world. At first, I felt "no way", but (1) the authors analyzed only books for adults (fantasy and scifi totally rule YA), (2) many fantasy books happen in our world or have connections to it, and (3) you can still pull it off if you do a great job with the plot and the characters like RR Martin. <br /><br />I highly recommend this book. Enjoy!<br /><br />P.S. The last chapter is quite interesting, too. So if you can teach computers to read books and make conclusions about them, can you teach them to write? It seems we are still in the very early stages of that. But, it'll happen some day with artificial intelligence. I think we are still ways off from that. <br />

There's an observation that sometimes goes around about how you only need to read the fourth chapter of any given business book. The first is an introduction, the second is about how everything you thought you knew about the subject was wrong, the third is the miraculous tale of how the authors came up with this new secret answer, and the fourth is the actual content. After that it goes into testimonial-style case studies and other rather dull stuff. So, the fourth chapter, or sometimes I've heard the fifth, is the only one you need to pay attention to. Either way, the point is that there's a certain class of non-fiction book that's mostly padding with an article's worth of actual content. The Bestseller Code: Anatomy of a Blockbuster Novel felt like one of those books to me.<br /><br />The Bestseller Code's been modestly controversial since it's publication, either because it's heretical to declare that there's a machine-identifyable set of characteristics to making a bestseller or because everyone already knew what those characteristics were, so who needed the computer? I tend to disagree with both critiques. First of all, if software can identify the patterns that lead to success then better we become aware of it than pretend the publishing business is driven entirely by artistic impulse. Second, if such a secret cluster of characteristics exists, then the monumental pile of unsuccessful novels that come with major publisher backing is evidence that most people in the industry don't know what they are.<br /><br />The problem is that The Bestseller Code isn't really going to show you one way or another, because while the book repeats the same mantra over and over (and over) that "our model predicted bestsellers within our sample with 80% accuracy!!!" there's actually very little evidence given. The data isn't there for one to consider, nor is the entire list of topics nor their full rankings within the model. So while I've no doubt that the authors successfully built a set of algorithms to measure a given text's likelihood of hitting a bestseller list, slogging through their 240 page advertisement for it is a pretty unsatisfying read. There just isn't a lot of detail or actual information on offer. Sure, there are a few generalizations (write short, simple sentences with lots of contractions that deal as much as possible with the topic of human connection and if you can get "I" and "him" in close proximity your on the right track), but they're precisely the ones you'll find brought up everywhere. Such sage advice as sticking to no more than three main topics in your book and not over-writing your sentences doesn't require an algorithm – you hear it all the time.<br /><br />So the problem isn't that the authors are wrong or haven't discovered something intriguing – and perhaps extremely useful – about the nature of bestsellers; the problem is that they really aren't sharing much of it in this book. That's a logical tactical choice if you plan on going into the business of getting people to pay you to run their books through your software, but it doesn't make The Bestseller Cods: Anatomy of a Blockbuster Novel a very useful or engaging read.

Using a computer algorithm, the authors of this book as the question of whether you can predict whether a novel will be a bestseller or not. Jodie Archer is a former publisher and consultant, while Matthew Jockers is the co-founder of Stanford University’s famed Library Lab. In this work they claim they can discover a bestseller and analyse 20.000 novels to demonstrate this.<br /><br />Subtitled, “Anatomy of the Blockbuster Novel,” this book attempts to analyse novels from the points of view of theme, plot, style, character and all data points. Of course, much of this is fairly obvious, as are the results of computer generated writing. For, if a computer can analyse what works within a novel, why can they not write that elusive bestseller?<br /><br />Overall, this is an interesting looks at the mechanics of writing and publishing, our obsession with lists and ranking and the anatomy of what creates a perfect story. The book also contains a list of 100 novels it believes you should read – as an avid reader I have read only six of them and the books which are missing include every classic. However, as the title suggests, this algorithm aims to discover that bestseller – the book that is in every supermarket and is the talked about novel for a certain amount of time. Some may become classics, others may not wear as well and that is why, thankfully, literature is based on more than commercial success. An interesting exercise though and a fun analysis of the bestseller charts.<br /><br />

The title of this book has it all for me...it's the reason I picked it up in the first place. The idea that blockbuster novels all share some elemental DNA in common is at once exciting and dangerous.<br /><br />I found that the authors of this book set out to prove their algorithm without giving away too many of the intricate details (likely proprietary information) and for the most part made their case in a concise and believable manner.<br /><br />For the most part.<br /><br />I honestly would've liked to have seen more actual numbers produced from their research contained within the pages of this book. For budding novelists out there let me spare you the time; this book will not deliver that one crucial element or secret to you that will make you a bestseller. It will get you looking at the number of times you use the word "the" and how often you use pronouns or Mr. King's dreaded "ly" words. It will tell you how bestselling authors write...or it will try to tell you.<br /><br />At the end of the day the machine can data mine thousands of best sellers but in my opinion never uncover the "secret sauce". Talent with words goes beyond their placement within a sentence of the size of one's paragraphs. What all best sellers really have in common is a love of language and a love of writing.<br /><br />And I've yet to come across the machine that can understand that...yet ;)

<br /><i>"Recommending a book is not like recommending a health tip or a stock. Recommending a book can be like trying to navigate the unspoken rules and faux pas of a Jane Austen ballroom. The book world comes with considerable baggage."</i><br /><br /><br />Who can explain what makes for a best-selling book? What techniques do best-selling authors employ that makes their works so desirable compared with the majority of authors who struggle for readership? Do those who write literary classics differ so much from those who appeal to the mass market?<br /><br />All this -- and more -- is covered in this fascinating look at big data and the New York Times best-seller list. There are actually two fascinating parts to the book. The first part covers the actual results of the study: that is, what makes for a best-seller? Second and equally interesting is how the big data on books are collected and interpreted. <br /><br />A side look at the interests and predispositions of readers at both the literary and mass market levels are fascinating as well. Who among literary lovers has not succumb to a mass market book despite their professed inclination otherwise? (For example, I count Dan Brown and The Da Vinci Code as among my particular weaknesses although I don't usually find what I'm going to read from among the typical household names on the best-seller list.)<br /><br />Is that unnecessarily snobbish? Am I no better than a fashionista who refuses to wear anything without a Prada or Hermes label? Or a foodie who refuses to eat anything that isn't farm-to-table? Book people, to thine own self be true. Those best-sellers are popular for a reason, and the best-selling formula can truly be seen on display here. It's a fascinating look, too, at why machines can't (at least not yet) replace writers, despite all that they can do otherwise.<br /><br />There's so much to think about in this short book...as well as plenty of reading suggestions for those book list compilers. It demonstrates very well what big data has yet to teach us about so many things, beginning with one of our favorites -- what we read.<br /><br /><br />My thanks to Good Reads and St. Martin's Press for allowing me to read this book!

The authors analyze the elements of bestselling novels, and arrive at some surprising conclusions. Fascinating.

Did they get high and write this?<br /><br />Jesus. This could've been so much better. They had all of this great data and then just dragged the fuck out of every chapter. . .and when the actual date was presented. . .it was fast and in clumps of undecipherable paragraphs.<br /><br />Great Discoveries. <br />Horrific Presentation. <br />And suck ass drag-on writing.

Кілька вчених-лінгвістів мали натхнення та багато вільного часу і створили програму, яка аналізує книги. Вони пропустили через неї величезний корпус текстів, навчили аналізувати їх стилістику та сюжет. Ідея спрацювала! Так з'явилась книга "Код бестселера". Що в ній особливого?<br />⠀<br />?Книга подає узагальнені висновки аналізу бестселерів New York Times: що об'єднує топові книги? Чому "50 відтінків сірого" і "Код да Вінчі" знаходяться поруч?<br />⠀<br />?В ній багато списків! Лідери по сюжету, стилістиці. Читаючи книгу треба тримати при собі олівець і блокнот для виписування назв книг.<br />⠀<br />?А ще є графіки по книгам, таблиці, числові значення на основі аналізу сюжетів... Ніколи не думала, що таке можливо.<br />⠀<br />В чому ж секрет бестселерів? Ключові фактори, котрі вдалося виокремити вченим:<br />⠀<br />?СТИЛЬ<br />⠀<br />Стилістика, граматика — це важливо. Інколи не так важливо, ЩО ви пишете, а те, ЯК. Бо ідей насправді нових не так багато. Тому стиль визначає письменника.<br />⠀<br />?НАЗВА<br />⠀<br />Цікаві роздуми подані про топові назви: чим відрізняється ефект використання артиклів а і the на читача? Чому такі популярні книги, в назві яких є слово "дівчина?" Яка назва містить загадку, а яка змушує читача пройти повз? ⠀<br />?ПОЧАТОК<br />⠀<br />Це мегацікава тема! На прикладі почптків бестселерів і класики розбираються фрази, які чіпляють читача на гачок. Вдалий початок — частина успіху.<br />⠀<br />?СЮЖЕТ<br />⠀<br />Сюжет повинен бути динамічним! Книга пропонує формулу відсоткового співвідношення різних тем. Що лежить в основі бестселерів? Які теми турбують читачів? Підказка: це є в "50 відтінках сірого" і це не секс?<br />⠀<br />Книгу читати було легко, приємно і цікаво. Автори поставили перед собою дуже амбітні цілі, провели ґрунтовне дослідження і представили його нам з купою актуальних прикладів і посилань.<br />⠀<br />Рекомендую!?<br />⠀<br />А вам запитання:<br />⠀<br />?Як гадаєте, яка тема в топі за результатами програмного аналізу?<br />⠀<br />?Яку книгу комп'ютер визначив як найнайвідповіднішу "стандартам" бестселерів? Я була здивована, бо раніше про неї не чула, тепер хочу прочитати?<br />.<br />Поскріню деякі книгосписки в телеграм?