Lab #1

Isabella Bossa and Harris Pollack.

  1. What kinds of patterns are being examined and how are they being measured in the projects found at the Stanford Literary Lab?

Modeling Dramatic Networks: Drama is the framework but the researchers are interested in relationships (networks). Specifically, how they grow and represent the connective tissue between people, objects, and cultural productions.

Suspense: Language, Narrative, Affect: Emotional patterns within texts that readers feel in anticipation of imminent events. The researchers are testing to see if the patterns that lead to the feeling of anticipation are similar in different time periods and genres involving literature.

The Emotions of London. Examines patterns in the emotions felt by characters in eighteenth- and nineteenth-century English novels are their geographic location.

Representation of Race and Ethnicity in American Fiction, 1789-1964. Examines the biological, geographical, and social perception of different ethnicities across the nation. Additionally, analyzes the terms used to describe different ethnicities over time and how these terms evolved. Lastly, examines the patterns between the “background” racial discourse and the representations of racially marked characters.

Reading Norton Anthologies: Canons and Trajectories: This project analyzes text included in each addition of various Norton series since the author M.H. Abrams edited the first Norton Anthology edition in 1964. This project will help explore the ways individual authors and works, along with broader trends of inclusion and exclusion in the Norton’s canon.

The Performance of Character: This project explores gender through the dialogic speech in novels as gendered performances by the author of the text. For example, how does a male author portray a female character. Researchers gain deep insight into the role that gendered dialogue plays in the creation of a novel.

Fanfiction: Generic Genesis and Evolution: This study analyzes the development and progression of the fanfiction genre with two decades worth of fanfiction. This data allows for the tracking of both authorial and readerly influence, the development of generic innovation, and the genesis and evolution of specific archetypes and stylistic conventions.

Trans-Historical Poetry Project: This project’s objective is to analyze the evolution of English-language poetry and how it changed across time. It examines patterns in phonetics, natural language, and statistics. It allows us to come up with ways to improve current theories of meter.


  1. Review the visualizations listed below.  What makes these visualizations successful?

The design is very eloquent and beautiful. However, there are many features that the tools are lacking. For example, the information is static. It is not fluid. But it is successful because it is interactive!

All the images are aesthetically pleasing. Moreover, the contrasting colors and the minimalist design make it easier for the user to focus specifically on what the image is trying to highlight. For example, in Bryan Christie’s heart illustration, all the attention goes to the heart because of the contrasting palette (grey versus red) used and because there really is not much else to see but the heart.

This is not pretty at all. This website is easy to follow and easy to use in regards to user experience. It is arcane in features and design. Not very aesthetically pleasing.

The visualizations are useful to understand major patterns in the novel and connections among characters, words, and places that might not be so obvious. However, some of the graphs (for example, the images on the left-hand side of the Radical Word Connections) contain too much information, which can make it hard for the user to absorb the information the graph is trying to convey.


How would you measure their success?  If you had to develop a list of features that make these visualizations successful, what might those include?

The most successful visualizations are those that are user friendly, practical, and use aesthetically pleasing designs. For example, the graphs shown in “Make Grey Your Best Friend” are the most successful ones because they are able to convey information in a way that highlights what is important, are easy to navigate and to understand, and are visually pleasing. The CMAP Mobility graphs are successful to a certain extent – they have nice designs and convey useful information, but they are not particularly user friendly or easy to navigate (especially taking into account the existence of competitors with user friendly interfaces such as Google Maps). The graphs in Novel Views: Les Miserables do not have an outstanding design, yet the information they contain can be useful and maybe even illuminating for a researcher of the novel. Therefore, the visualizations are successful overall. HyperHistory online is the least successful one due to two main reasons: it has below-quality and outdated design and the information it contains can be easily accessed in other pages such as Wikipedia.


  1. Go to Dirt (Digital Research Tools) and choose one (1) tool listed under “Analyze Data” and one tool listed under “Visualize Data.”  How might these tools be useful in analyzing large amounts of data?

Sci2 Tool

“Modular toolset supporting temporal, geospatial, topical, and network analysis and visualization of datasets at the micro, meso, and macro levels.”

This tool lets its user(s) upload or create their own datasets (which helps to organize data), perform algorithmic analyses and/or create visualizations (which helps to understand data), and share information with other users.


“Allows the investigator to set up a Web map around a particular topic and invite multiple participants to contribute information the the map on their own time and from their own device.”

This tool allows for collaboration – one of the most important aspects of research. This collaboration might allow for users to discover new patterns and connections in the topic they are researching. The map will also allow researchers to put a lot of data together,  organize it in a (hopefully) concise image, and understand it better.