Seeing Patterns — June 3-9, 2019


In the previous module, we collected and cleaned vast amounts of historical data. Let's see what patterns emerge. Part exploration, part reflection, and part argument, these two stages are not linear but feed into one another. Exploration and preliminary visualizations are often about finding the holes. Sometimes, a quick visualization helps you understand what is missing, or what is a glitch in your method, or what is just plain mistaken in your assumptions. I once spent quite a lot of time staring at a network graph before I realized that I'd neglected to import the entire file - I was trying to interpret just a subset of it! Back to my wrangling I went, and in the process of cleaning that data, I realized there were patterns that would be better visualized as simple line plots. I visualized these - and found other errors. Back to wrangling. In the end, a combination of network graph and simple line plots allowed me to identify a story about influence and control of landholding (I was working with archaeological data).

In this module, let us begin by looking at a couple of pieces that explore this cyclical dynamic between data wrangling and exploration. In our Slack space for this module or in your notebooks, I want you to explicitly discuss the work of the following scholars. Make explicit connections with things you've read/explored/studied/developed in other history classes!

In essence: what would your last essay last term have looked like, if you'd been thinking along these lines?

  1. Begin by visiting the Mapping Texts project (PDF downloads in new tab).
  2. Look at the work of Michelle Moravec who is using corpus linguistics tools to understand the history of women's suffrage (and also the visualizing gender).
  3. Listen to Micki Kaufmann on Quantifying Kissinger. (Her methods are detailed on her blog.)
  4. Talk about network analysis.
  5. Keep an eye on Paul Revere. Talk about topic modeling (and also this blog post on Mallet).
  6. Talk about principles of visualization.
  7. You should probably talk about Edward Tufte, too.

Talk. What could history be like if more of our materials went through these mills?

Things you will learn in this module

  • Importing, querying, and visualizing networks with Gephi <- igraph, R
  • Topic modeling with the GUI topic modeling tool, and MALLET at the command line, and in R
  • Simple maps with CartoDB (which may include georectifying and displaying historical maps as base layers)
  • Corpus linguistics with AntConc
  • TF-IDF with Overview
  • Quick visualizations using RAW, an open source data visualization framework

We will be busy in this module. Do not be afraid to ask myself, your peers, or the wider DH community for help and advice! It's the only way we grow.

And finally...

Just because there is a package, or a routine, or an approach to doing x with your data does not mean that you park your critical apparatus at the door. Consider Matthew Jockers who has done some amazing work putting together a package for the R statistical language that helps one analyze plot arcs in thousands of novels at once. Jockers describes how it works on his blog. Annie Swafford explores this package in a comprehensive blog post. She highlights important issues in the way the package deals with the complexity of the world, and how that might have an impact on results and conclusions drawn from using the package. The interplay of the work that Jockers has done, and the dialogue that Swafford has started with Jockers, is an important feature (and perhaps, a defining feature) of digital humanities scholarship. Jocker builds; and Swafford approaches the code from a humanistic perspective; and in the dialectic of their exchange, the code and our ability to understand the code's limitations and potentials will improve. And so we understand the world better.

When you choose your exploratory method, you need to consider that the method has its own agenda!

What you need to do this week

  1. Respond to the readings and the reading questions through annotation (taking care to respond to others' annotations as well) - see the instructions below. Remember to tag your annotations with 'hist3814o' so that we can find them on the course group. Remember to annotate using our HIST3814o group.
  2. Do the exercises for this module, pushing yourself as far as you can. Annotate the instructions where they might be unclear or confusing; see if others have annotated them as well, and respond to them with help if you can. Keep an eye on our Slack channel - you can always offer help or seek out help there. The exercises in this module will pick up where we left off, with the Texan Correspondence. You'll represent the exchange of letters as a network. We'll try to extract place names from it. We'll topic model the letters themselves. We'll explore various ways of visualizing those results. Write a blog post describing what happened as you went through the exercises (your successes, your failures, the help you may have found/received), and link to your 'faillog' (ie. the notes you upload to your GitHub account - for more on that, see the exercises!).
  3. Submit your work to the course submission form.


Select one of the articles behind the links above (and/or in the exercises) to annotate. Ask yourself: who benefits from this? Who is hurt from this? Make an entry in your blog on this theme.