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Welcome to .txtLAB, a digital humanities laboratory at McGill University directed by Andrew Piper. We explore the use of computational and quantitative approaches towards understanding literary and cultural phemonena 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 the large and small scale.
Why are Jane Austen's novels so popular? Her characters are introverts.

Why are Jane Austen’s novels so popular? Her characters are introverts.

As part of the work on characterization in the novel that we’ve been doing recently in the lab, I’ve come across an interesting aspect of the classic nineteenth-century novel. It turns out that female main characters are far more cogitative and perceptive than their male counterparts. However, this appears only...
Latest entries
Announcing CA: Journal of Cultural Analytics

Announcing CA: Journal of Cultural Analytics

I am very pleased to announce the pending launch of CA: Journal of Cultural Analytics, an open-access web-based academic journal that will focus on the computational study of culture. CA’s mission is to use data-driven approaches towards the study of literature, culture and history. Our mandate is as capacious as it is focused: to transform...
Why do book reviews still treat women like it's the 19th Century?

Why do book reviews still treat women like it’s the 19th Century?

I have a new piece out with my collaborator Richard Jean So at The New Republic that explores gender bias in book reviews. Looking at a sample of 10,000 book reviews published in The New York Times since 2000, we found a disappointing story about how reviews of women’s books overwhelmingly skew towards family and...
Do Creative Writing Degrees Impact the Contemporary Novel?

Do Creative Writing Degrees Impact the Contemporary Novel?

I have a new piece out in The Atlantic with Richard Jean So. The piece addresses recent debates as to whether MFA programs have had a major impact on contemporary novels. The short version is that there is very little evidence to suggest any major differences between novels written by authors with MFA degrees and...
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...
Does the Canon Represent a Sampling Problem? A Two Part Series

Does the Canon Represent a Sampling Problem? A Two Part Series

The most recent pamphlet from the Stanford Literary Lab takes up the question of the representativeness of the literary canon. Is the canon — that reduced subset of literary texts that people actually read long after they have been published — a smaller version of the field of literary production more generally? Or is it substantially different?...
The Constraints of Character. Introducing a Character Feature-Space Tool

The Constraints of Character. Introducing a Character Feature-Space Tool

What is it that we do with characters? And what do they do for us? Different schools of literary theory have provided different answers to these questions. For the Russian formalists, character was above all else a “type,” one that served different narrative functions, a move that has been recently reawakened in the field of...
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...
txtLAB450. A Multilingual Data Set of Novels for Teaching and Research

txtLAB450. A Multilingual Data Set of Novels for Teaching and Research

I am very pleased to be able to share a collection of 450 novels that we have assembled that were published in English, French, and German during the long nineteenth century (1770-1930). The novels are labeled according to language, year of publication, author, title, author gender, point of view, and word length. They have been labeled as well...
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...
How I predicted the Giller Prize (and still lost the challenge)

How I predicted the Giller Prize (and still lost the challenge)

This Fall we created a lab challenge to see if anyone could predict this year’s Giller Prize winner using a computer. The winner was announced last night, and it turns out I correctly predicted the winner. But I still lost the challenge. In this lies an instructive tale about humans, computers, and predicting human behaviour....