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Welcome to .txtLAB, a laboratory for cultural analytics at McGill University directed by Andrew Piper. We explore the use of computational and quantitative approaches towards understanding literature and culture in both the past and present. Our aim is to engage in critical and creative uses of the tools of network science, machine learning, or image processing to think about language, literature, and culture at both large and small scale.
Posts tagged "bestsellers"
How Cultural Capital Works: Prizewinning Novels, Bestsellers, and the Time of Reading

How Cultural Capital Works: Prizewinning Novels, Bestsellers, and the Time of Reading

This new essay published in Post45 is about the relationship between prizewinning novels and their economic counterparts, bestsellers. It is about the ways in which social distinction is symbolically manifested within the contemporary novel and how we read social difference through language. Not only can we observe very strong stylistic differences between bestselling and prizewinning writing,...
CBC interview on using algorithms to predict prizewinners and bestsellers

CBC interview on using algorithms to predict prizewinners and bestsellers

This past weekend I participated in an interview with Jeanette Kelly on the CBC to discuss our new work on using computers to predict bestsellers and prizewinning novels. In it I discuss the Devoir challenge in which local Quebec writers try to impersonate a bestseller using our data and our successful attempt at predicting this year’s Giller Prize winner...
Interview with BookNet Canada on algorithms, publishing and creative writing

Interview with BookNet Canada on algorithms, publishing and creative writing

I recently did a podcast with the BookNet group in Canada that focuses on the intersection of technology and books. They were interested in our research focusing on prizewinning and bestselling novels. My main emphasis in the discussion was to focus on the way computers can be useful for different kinds of audiences: for publishers to better understand the books...
The .txtLAB Guide on How to Write Like a Bestseller

The .txtLAB Guide on How to Write Like a Bestseller

Here is a humble 1-page guideline that we produced after studying a sample of 10 years worth of the bestselling novels according to the NY Times Bestseller list. It was used as part of the Devoir Challenge in which some local Montreal writers were asked to try to write stories “like an American bestseller.” One of the most...
The Devoir Challenge. How to write like an American Bestseller

The Devoir Challenge. How to write like an American Bestseller

When the books editor of Le Devoir, Catherine Lalonde, called to ask if my lab would supply a data-driven guide on how to write like a bestseller, I enthusiastically said yes. But I expected everyone else would say no. Surely writers will be allergic to data. And surely Quebecois and Canadian writers won’t want to write like...
Quantifying the Weepy Bestseller

Quantifying the Weepy Bestseller

I have a new piece out that is appearing in The New Republic. In a number of recent book reviews, literary critics and novelists arrive at the consensus that to be a great writer, one must avoid being “sentimental.” One famous novelist describes it as a “cardinal sin” of writing. But is it actually true? Using a computer science method...
Prizewinners versus Bestsellers. Timeless Reads or the Spotlight of Fame

Prizewinners versus Bestsellers. Timeless Reads or the Spotlight of Fame

This post is the first in a series by this year’s .txtLAB interns. It is authored by Eva Portelance. Building Corpuses The first step in our search for answers required that we build solid corpuses for comparison. The PW corpus was selected from five main literary awards given in the United-States, Canada and Britain. These...