I recently gave a workshop for the US Latino Digital Humanities Center (USLDH) at the University of Houston on introductory text analysis concepts and Voyant. I don’t have a full talk to share since it was a workshop, but I still thought I would share some of the things that worked especially well about the session. USLDH recorded the talk and made it available here, and you can find the link to my materials here.
I had a teaching observation when I was graduate student, and one comment always stuck with me. My director told me, “this was all great but don’t be afraid to tell them what you think.” I’ve written elsewhere about how I tend to approach classroom facilitation as a process of generating questions that the group explores together. This orientation is sometimes in conflict with DH instruction, where you have information that simply needs to be conveyed. I had this tension in mind while planning the USLDH event. It was billed as a workshop, and I think there’s nothing worse than attending a workshop only to find that it’s really a lecture. How to balance the generic expectations with the knowledge that I had stuff I needed to put on the table? As an attempt to thread this needle, I structured the three-part session around a range of different kinds of teaching moves: some lecture, yes, but also a mix of open discussion, case study, quiz questions, and free play with a tool.
The broad idea behind the workshop entitled “Book Number Graph” is that people come to text analysis consultations with all varieties of materials and a range of research questions. Most often, my first step in consulting with them is to ask them to slow down and think more deeply about their base assumptions. Do they actually have their materials in a usable form? Is it possible to ask the questions they are interested in using the evidence they have? I built the workshop discussions as though I was prepping participants to field these kinds of research consultations, as though they were digital humanities librarians.
First, the “book” portion of the workshop featured a short introduction to different kinds of materials, exploring how format matters in the context of digital text analysis. We discussed how a book is distinct from an eBook is distinct from a web material, and how all of these are really distinct from the kind of plain text document that we likely want to get to. I used here a hypothetical person who shows up in my office and says, “Oh yeah, I have my texts. I’m ready to work on them with you. Can you help me?” And they will hand me either a stack of books or a series of PDF files that haven’t been OCR’d. I introduced workshop participants to the kinds of technical and legal challenges that arise in such situations so that they’ll be able to better assess the feasibility of their own plans. This all built to a pair of case studies where I asked the participants how they would respond if a researcher came to them with questions for their own project.
With these case studies, I hoped to give participants a glimpse into the real-world kinds of conversations that I have as a DH library worker. For the most part, consultations begin with my asking a range of questions of the researcher so as to help them get new clarity on the actual feasibility of what they want to do. I hoped for the participants to question the formats of the materials for these hypothetical researchers and point out a range of ethical and legal concerns. Hopefully they would be able to ask these questions of their own work as well.
For the second section of the workshop entitled “number,” I gave participants an introduction to thinking about evidence and analysis, distinguishing between what computers can do and the kinds of things that readers are good at. Broadly speaking, computers are concrete. They know what’s on the page and not what’s outside of it. Researchers in text analysis need to point software to the specific things that they are interested in on the page and supplement this information with any other information outside of the text. Complicated text analysis research questions have at their core really simplistic, concrete, measurable things on the page. You are pointing to a thing and counting. For examples of the things that computers can readily be told to examine, we discussed structural information, proximity, the order of words, frequency of words, case, and more.
To practice this, I adapted an exercise that I was first introduced to by Mackenzie Brooks but that was developed by librarians at the University of Michigan. To introduce TEI, the activity asks students to draw boxes around a printed poem as a way to identify the different structural elements that you would want to encode. For my purposes, I put a Langston Hughes poem on the Zoom screen and asked participants to annotate it with all sorts of information that they thought a computer would be capable of identifying.
The result was a beautiful tapestry of underlines and squiggles. Some of the choices would be very easy for a computer: word frequency, line breaks, structural elements. But we also talked about more challenging cases. We know the poem’s title because we expect to see it in a certain place on the page. The computer might be pointed to this this by flagging the line that comes three after three blank line breaks. But what if this isn’t always the case? It was good practice in how to distinguish between the information we bring to the text and what is actually available on the page. We talked about the challenges in trying to bridge the gap between what computers can do and what humans can do, to try and think through how a complicated intellectual question might take shape in a computationally legible form.
Wrapping all this together, I introduced what I called the M.E. test for text analysis research. To have a successful text analysis project you have to have…
- Materials that are…
- appropriate to your questions and
- accessible for your purposes.
You must also have
- Evidence that is…
- identifiable to you as an expression of your research question and
- legible to the tool you are using.
Materials and Evidence. M and E.
M.E.
The next time you sit down to do text analysis, ask yourself, “What makes a good question? M.E. Me!”
Painfully earnest? Sure! But this was a nice little way for me to tie in what I often joke is my most frequently requested consultation topic: imposter syndrome. The M.E. question is both a test for deciding whether or not a text analysis research question is appropriate, but it is also a call for you to recognize that you can handle this work. A nice little way for you to give yourself a pump up, because I believe that these methods belong to anyone. Anyone can handle these kinds of consultations. They’re more art than science at the level we are discussing. You just have to know the correct way to approach them. Deep expertise can come later. If you are too intimidated to get started you will never get there.
From there, I closed the “number” portion of the workshop with a couple more case study prompts. I asked participants to respond to two more scenarios as though someone had just walked into their office with an idea they wanted to try out.
The hypothetical consultation prompts involved, first, an interest in finding the most important characters in a particular Shakespeare play and, second, an interest in space and place in southeastern American literature. In each case, we discussed questions of format and copyright, but we also got to some fairly high-level questions about what kinds of evidence you could use to discuss the research questions. For importance, participants proposed measuring either number of lines for each character or who happens to be onstage for the greatest amount of time. For space and place, we discussed counting place names using Python (a nice way to introduce concepts related to Named Entity Recognition). In each case, my goal was to give the workshop participants a sense of how to test and develop their own research questions by walking them through the process I use when talking with researchers asking for a fresh consultation.
USLDH has shared the recording link, so feel free to check out the recording if you want to see the activities in action. The slides can be found here. And never forget the most important thing to ask yourself the next time you’re working on a text analysis problem:
“What makes a good research question? Me.”
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