Meet our new User Interface Developer -- Catherine Winters

This fall, the Lab is very happy to welcome a new User Interface Developer to the team! Catherine Winters joined us this September, and I talked with her about her path to the DHIL, designing cooperative interfaces, creating Twitter bots, and growing up with the World Wide Web.

Kim: Hi Catherine! So my first question is, what does a User Interface Developer do?

Catherine: Hi! Well, I make our Digital Humanities projects easier to use by improving their user experience.

When did you first learn to code, and how?

I guess when I was about 8, by reading '80s computers books, but the things I actually credit with being more important than that were a couple of education games from the '80s, like Rocky’s Boots for the Apple IIC, which taught kids about gate logic. I also played Robot Odyssey, which was more popular and well-known, but also much harder. And Pinball Construction Set, which allowed you to use the mouse to build objects that change the style and appearance, so you could wire it up with logic and in the end you had something on your floppy disc that could play a game. In terms of stuff that I still use today, I taught myself HTML in 1994, when about half the sites on the internet at that point were about how to teach yourself HTML.

When did you first start working as a developer, and what was your path there?

There weren’t a lot of people that were good at computers in the 90s, and the path by which a lot of people become web designers and web developers was that they were the ones who had Photoshop. There was no standard path from computer science major to then getting a job at a magazine making websites. Instead, people asked if you knew how to do a particular thing, and if you could make them a website, and that’s how it happened back then. My story was very similar, in that I had those skills and people hired me for them.

How did that path first intersect with Digital Humanities? How did you first hear about it, or start working in it?

Data science was something I’ve been aware of since the early 2000s, but since then, I’ve mostly worked for different universities and companies, making brochure sites and application forms, proving that it’s always more interesting what you can do with computers than computers themselves. That comes back to my interest in user experience; I believe a good interface is one that you don’t have to fight with.

I saw on Twitter that you’ve made a few bots...what do your favourite ones do?

My favourite one is @Gastwon, a parody account of tweets that real people have made about Gastown (in Vancouver, BC) and the surrounding environs. What I enjoy about it, is that it gets weird retweets from random people. The trick for this, of course, was that a lot of the people talking about Gastown were awful, and there was some ‘data massaging’ required to make sure that in a whole bunch of searches about Gastown locations, none of them were racist, none of them were classist, no slurs. People here are generally pretty terrible, and Twitter amplifies this, and the result of this needed to be a bot that addresses facts of Gastown but doesn’t actually harm people. It’s been in the media a couple of times, but no one has ever contacted me about this ever -- I only find this out when suddenly the follower count jumps.

When you say "data massaging", is that something programmed into the bot itself?

No, @gastwon just tweets things “inspired” by other people’s tweets. I did that part manually.

@officefridgebot, on the other hand, is completely fictitious. It remixes email topics and complaints about the office fridge. It’s constructed using a self-assembling grammar; the software is called Tracery, by Kate Compton, currently a grad student interested in emergent and generative narratives. The latest version of @officefridgebot runs on Glitch, which is a fine successor to those tools that I enjoyed when I was a kid.

There’s a JSON file that defines the grammar, which it then combines with the complaint template. There are options for random varying degrees of severity of intention; for example, the option to personalize the complaint with a name or gossip angrily about a specific food. Those names and foods are pulled from these giant long lists of text corpora Darius Kazemi assembled on GitHub. The end result has been that sometimes I learn about a new food, like I did with Jerusalem artichokes.

There are other folks in the Digital Humanities making bots like this -- for example, Tiffany Chan from the Digital Scholarship Commons at UVic has a fun bot also powered by Tracery. It’s @TaySEliot, which is a Taylor Swift / T. S. Eliot mash-up bot. She also provides the source code for the whole thing, which is actually pretty exhaustive when it comes to source material--both Eliot and Swift. The hard part is just writing the list of text, because the rest is already taken care of by Tracery. @TaySEliot  is powered by a website called Cheap Bots Done Quick, which takes care of it all for you.

What are some of the things that you are currently working on for the Lab right now? What’s a part of your day to day?

One of the things I’ve been working on is improving the useability of text-string comparison on our eXist sites, to reduce the number of clicks, and use a couple of well-known tricks to make it more straightforward for people to use. It’s called Comparalator (title probably to change).

I’m also working on some fun data visualizations which I’m looking forward to showing off in the next few weeks.