November 30, 2022
Can Big Data Predict the Next Bestseller?

Can Big Data Predict the Next Bestseller?

Everyone has started to tap into the potential of big data, whether it’s in the music industry, the sports world, or the IT sector.

The publishing sector should also not be disregarded. Similar to the music industry, publishing thrives on the backs of commercially successful works.

But predicting blockbusters is difficult. It’s always been a difficult art form to fully understand, and only the most perceptive critics and publishing organizations have been able to do so.

There are occasions when these faculties can be useful to the business, but when it comes to first-time authors, they virtually invariably get things wrong.

How do you combat this problem?

If only there were a computer system that could consistently predict best-selling literature with an accuracy of 80% or higher…

Sure, we do. A new book explores the concept of a bestseller-o-meter. Former Apple research lead on literature Jodie Archer and current English associate professor at the University of Nebraska-Lincoln Matthew l. Jockers have authored The Bestseller Code: Anatomy of the Blockbuster Novel. The algorithm’s stated result is based on 30 years of history of correct predictions of New York Times bestsellers.

The workings – How does this algorithm work?

Looking at a vast body of literature, such as more than 20,000 books, can help determine what makes popular fiction popular. This is what the bestseller-ometer aims to do. This work provides a counterargument, based on statistics, to the common belief that there are a few key ingredients to writing a best-seller. But this does suggest that in the future, publishers may use this technology to assist them skip around the tried-and-true techniques of choosing a prospective bestseller.

The dawn of an idea – but was it really?

Jockers and Archer’s technique isn’t the first attempt to use big data to enhance literary excellence. Berlin-based firm Inkitt, responsible for the “first novel picked by an algorithm,” carefully tracks user feedback to determine which stories have the highest chance of becoming blockbusters on their website.

Jellybooks, a London startup formed in 2011, uses software that consumers download in return for early access to a novel to monitor reader participation in the literary creation cycle.

The bestseller-ometer, on the other hand, stands out because it combines academic rigor with computational might to determine which books are the most popular. At the level of diction and syntax, The Bestseller Code shows the careful considerations that went into teaching a computer to read and unpacking the microdecisions involved in producing best-selling literature.

Algorithms are a reflection of the analytical and interpretative choices made while reading a particular book closely. Allusions, word choices, themes, and patterns of repetition are all scrutinized.

The elements – What the algorithm uses

The algorithm commonly uses formal, authoritative “voice,” as well as concise, plainspoken, often colloquial text and declarative verbs that signify active, in-charge characters.

Narrative cohesiveness, as identified by Archer and Jockers, is the other, rarer component. Storyline cohesiveness is a trademark of best-selling writers. Typically, the focus on law and attorneys occupies around a third of John Grisham’s works.

The secret to bestselling – according to bestseller-ometer  

Unanticipated findings emerged as well, such as the fact that sex is not a popular commodity. In truth, this is a highly divisive issue for the general public, hence it is rarely included in the most popular books.

Consider the novel Fifty Shades of Grey, which included both shocking eroticism and a shocking turn of events. Therefore, it is surprising that this book became so popular.

In spite of this, Jockers and Archer realized that the book’s core topic and subject was human connection, a concept shared by all blockbusters. The novel’s success can be attributed to its focus on the development of romantic relationships between its protagonists.

The drawback

Because established authors like J.K. Rowling and John Grisham command such high royalties, publishers are generally unwilling to take chances on newcomers. The bestseller-ometer can help with that. However, this algorithm does raise the worry that content can be produced to fulfill the algorithm’s demands even if the creator of the content has no expertise in literature.

We may either have a great book that deserves to be a bestseller, or we can have a terrible book that satisfies the requirements of the algorithm but not the standards of literature.

Conclusion

As I’ve mentioned, big data is seeingp into every nook and cranny of our daily lives. Seemingly, demand rises in tandem with consumption, regardless of the sector.

Big data is full with exciting new challenges every day. Tasks include working with data, finding problems, fixing them, and making predictions about the future. Get your big data certification and make your way into this dynamic industry if you find yourself drawn to it.