Mikaela Fudolig is a postdoctoral associate working under Professor Peter Dodds and Professor Chris Danforth in the Computational Story Lab (CompStoryLab). She is working on the lab's newest big project on Ousiometrics and Telegnomics, which are defined to be the study of the essence of meaning and remotely sensed knowledge, respectively. Work done in the middle of the 20th century aimed to quantify meaning through orthogonal dimensions (or "independent" dimensions). Words have been thought to be characterized by three scores: valence, arousal, and dominance (VAD). Over the years, research has mainly been in the direction of finding these scores for a greater variety of words, but the VAD framework has been largely left intact. By re-examining large-scale VAD lexicons, the group has found that the essence of meaning can be further reduced into two dimensions: power and danger. Further, various real-world English language corpora—literature news, Wikipedia, talk radio, and social media—all exhibit a systematic bias towards safe, low-danger words.

The lab has several projects in the pipeline that will expand on the power-danger framework. Mikaela is working on the temporal aspect of ousiometrics in books. Specifically, she's studying how power and danger change as one reads through a book, and how this temporal behavior changes with the type of book one is reading. She hopes that her study will help shed light on the use of language and meaning in literature.

Though she is very busy in the lab, we had the opportunity to ask Mikaela a few questions about herself and clarify some points about her research.

Where did you do your PHD?

Mikaela: I got my PhD in physics from the University of the Philippines.

How long have you been at UVM as a Postdoc?

Mikaela: I've been a postdoc here since November 2020. As postdocs, our positions are temporary—so, my contract ends in 2023.

Is the work that you are doing now in the Computational Story Lab closely related your previous research interests while a doctoral student?

Mikaela: Broadly, yes! I've always been interested in using mathematical techniques for applications in the social sciences. The Computational Story Lab does exactly that.

I know that you recently had a preprint for an article. Do you know yet when the full article will be published?

It's still in review, so maybe in a few months.

Could you expand a bit (in layperson's terms) on what is meant by "orthogonal dimensions?"

Mikaela: By "orthogonal", we mean that these are "independent" dimensions. For example, the x- and the y-axis, as we usually define them, are independent. A point defined by its (x,y) coordinates can be moved by changing x or by changing y, and these are independent of each other. But if you define a point by (x, z), with z=2x [which is a diagonal line somewhere between the x- and y-axes] then you can't change x without changing z, so x and z are not independent.

So, going back to the scores mentioned in the previous text about my research (and the pre-print)…let's imagine a 3D space (say, a box) and put words in there depending on their VAD scores. The aim was to show that valence, arousal, and dominance are not independent of each other, but you can try to find other axes that explain how the words are oriented, and these are the power, danger, and structure scores.

Who uses the VAD lexicons, and what are they hoping to find/demonstrate/provide?

Mikaela: The VAD lexicons are commonly used by those who do sentiment analysis. There are other lexicons too, like the labMT happiness lexicon, also developed by CompStoryLab.

Your work studying how power and danger change as one reads a book is very intriguing. Are you imagining that a better understanding of this will influence how writers intentionally put together (or think about putting together) a story/text?

Mikaela: Possibly, but we're also more interested in how this is all likely unintended. Maybe that's just how language works, or maybe that's how *good* use of language works—we have to figure that out!

How has the fact that so much text is now easily accessible with a few clicks changed the possibilities in analyzing text such as what you are doing?

Mikaela: The age of big data has really opened up a lot of avenues for research. Whereas before you had to manually comb through text and thus be limited by your sample size, now you can automatically analyze thousands, millions, billions of stuff, like tweets, books—anything available online really.

Is there an interdisciplinary aspect to your research (or potentially)? I can imagine the folks in the languages, linguistics, and English departments finding your work very interesting?

Mikaela: The Vermont Complex Systems Center, which houses the CompStoryLab, is an interdisciplinary center. We have folks from math, computer science, physics, and philosophy who work together on problems that require expertise in several areas. We've also had collaborations with other folks from the social and natural sciences who are not part of the Center.

What direction will you take with your career after your time at UVM (public/private work, full-time research, corporate, teaching...a combination?)

Mikaela: I haven't decided yet—we'll see this coming year!