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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.
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?...
Latest entries
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

This past week featured an inspired challenge created by the book editor at Le Devoir, the French language newspaper here in Montreal. In it Catherine Lalonde asked well-known Quebec writers to “write like an American bestseller.” Our lab supplied them with a guide sheet based on our data analytics of bestselling novels published in the last decade. The...
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....

Can a computer predict a literary prize?

This evening the Giller Prize winner will be announced. For those not in the know, the Giller Prize is Canada’s most prestigious literary award. Like the Man Booker in the UK or National Book Award in the US, the Giller Prize serves as a way of signalling to Canadian readers important new fiction. It relies...

Intro to Literary Text Mining

It’s that time of year, so I’ve gone ahead and posted my new syllabus for Introduction to Literary Text Mining. It’s still a work in progress and probably always will be. However I’m beginning to get a sense of the various contours/spaces of the field and the ways those can be taught to students. The...
Detecting Literary Characters

Detecting Literary Characters

We are pleased to announce the acceptance of a new paper in this year’s Conference for Empirical Methods in Natural Language Processing (EMNLP-15). The paper offers additional methods beyond NER for identifying characters in novels. This work is part of our on-going project of studying social networks in fiction. As we’ve come to realize, just...
Development of a (Semi-) Automatic Character Network Tool

Development of a (Semi-) Automatic Character Network Tool

This is the third post in the series of .txtLAB intern projects. It is authored by Tristan Dahn. The concept of social network analysis – initially rooted in classical sociology and more recently in the social scientific, mathematic, and computer science realms – dates at least as far back as the mid 1960’s [7]. Classically,...
Upward Looking and Forward Thinking? The Stance of the Modern Novel

Upward Looking and Forward Thinking? The Stance of the Modern Novel

What would it mean for the novel to take a stance? To position itself relative to the world? How would it do so and how might we understand this positioning? At the individual level, we can imagine how certain novels are written from a particular orientation to the world,  from “below” as in the case of Notes from...
The Sweep of History

The Sweep of History

This is the second in a series of posts by .txtLAB interns. This post is authored by Magdalene Klassen. Many if not most contemporary historians would probably agree with the statement that “the typical mode of explanation used by historians [is] narrative.” (Roberts 2001) Storytelling, then, is not the difference between history and fiction. Instead, we...