Success In Books: A Big Data Approach to Bestsellers
Reading remains the preferred leisure activity for most individuals, continuing to offera unique path to knowledge and learning. As such, books remain an importantcultural product, consumed widely. Yet, while over 3 million books are published eachyear, very few are read widely and less than 500 make it to the New York Timesbestseller lists. And once there, only a handful of authors can command the lists formore than a few weeks. Here we bring a big data approach to book success byinvestigating the properties and sales trajectories of bestsellers. We find that there areseasonal patterns to book sales with more books being sold during holidays, andeven among bestsellers, fiction books sell more copies than nonfiction books. Generalfiction and biographies make the list more often than any other genre books, and thehigher a book’s initial place in the rankings, the longer the book stays on the list aswell. Looking at patterns characterizing authors, we find that fiction writers are moreproductive than nonfiction writers, commonly achieving bestseller status withmultiple books. Additionally, there is no gender disparity among bestselling fictionauthors but nonfiction, most bestsellers are written by male authors. Finally we findthat there is a universal pattern to book sales. Using this universality we introduce astatistical model to explain the time evolution of sales. This model not onlyreproduces the entire sales trajectory of a book but also predicts the total number ofcopies it will sell in its lifetime, based on its early sales numbers. The analysis of thebestseller characteristics and the discovery of the universal nature of sales patternswith its driving forces are crucial for our understanding of the book industry, andmore generally, of how we as a society interact with cultural products.
Santo Fortunato, Carl T. Bergstrom, Katy Borner, James A. Evans, Dirk Helbing, Stasa Milojevic, Alexander M. Petersen, Filippo Radicchi, Roberta Sinatra, Brian Uzzi, Alessandro Vespignani, Luda Waltman, Dashun Wang, Albert-Laszlo Barabasi