What can digital data tell us about theater performances?

by Briana Johnson on August 2, 2021

This is a guest post by Miguel Escobar Varela, author of Theater as Data: Computational Journeys into Theater Research. He is Assistant Professor of Theatre Studies at the National University of Singapore. Follow him on Twitter, @migueljogja. This volume is available for open access online reading and for purchase in hardcover and paperback. 

Performances are ephemeral and dynamic. At first glance, data might seem ill-suited to catch the complexity of the performing arts. However, performances – like much of our daily activities – leave digital traces behind, such as scripts, touring records, program booklets, playbills, videos, and reviews. When processed by computational methods, this data can reveal new insights about the reception of performances, the nature of artistic collaborations, the unique style of specific choreographers, the ebbs and flows of playwriting conventions, and the diffusion of performance traditions over time and space.

For me, the intersection of performance and computation is a powerful magnet. Since a young age, I have been drawn to both software programming and theater, and the digital humanities have given me a conceptual home from which to pursue these varied interests. Luckily, I’m in good company. Many before me have used data to study the history and current landscape of theater: Clarisse Bardiot, Debra Caplan, Sarah Bay-Cheng, Derek Miller, Christof Schöch, Peer Trilcke, Frank Fischer, Harmony Bench, Kate Elswit, Susan Wiesner, Joanne Tompkins, Jonathan Bollen, Julie Holledge and many others. Reflecting on the projects of other researchers, as well as on the work I have done with my collaborators, I identify two ways of working with data. In the first one, which I call data-driven methodologies, we use data to answer closed questions. We create a formal representation of a question and automate a sequence of procedures to provide an answer. The criteria for evaluation are defined beforehand, and the answer is measured against these criteria. In contrast, in data-assisted methodologies, we use data to transform our view of a problem. The purpose of framing a theatrical event as data is not to offer a clear answer but to augment our capacity to think about such an event. Data, in other words, provides a good defamiliarization strategy.

The same method can be used for either data-assisted or data-driven research. Let me illustrate this with two examples that use network analysis. Holledge et al. (2016) used a large dataset of performances of Ibsen’s A Doll’s House to show that a long chain connects productions from the late nineteenth century until the 1990s. In this directed network, edges indicate that at least one actor from a given production was involved in another, more recent performance. This network shows the movement of actors across different temporal layers of the playscript’s history. This effectively demonstrates that it is not only ideas, but specific people, that ushered A Doll’s House into global fame. Holledge et al.’s analysis shows the decisive influence of women in the production and promotion of the play—rather than merely in the portrayal of the protagonist—another unexpected and important finding. 

Network analysis can also be used for playful, data-assisted exploration. Take for example Brecht Beats Shakespeare! A Card-Game Introduction to the Network Analysis of European Drama (Hechtl et al. 2018). This is a card game where each card has a network visualization and measurements that correspond to different theater plays. The players try to win an opponent’s card with a higher value—but they must agree which network measurement (density, average eigenvector centrality, etc.) to use for the game. The game relies on networks to produce a creative defamiliarization of theater history.

The main distinction between these methodologies is the criteria they require for evaluating conclusions as useful and valid. The gold standard of data-driven research is replicable, incremental knowledge. To assess data-driven claims, we should ask how likely the results are to be true and whether other independent sources of evidence corroborate these conclusions. In contrast, data-assisted frameworks ask to be judged for their generative capacity: their potential to trigger novel questions and bring forth new perspectives. 

These are some of the ideas I explore in Theater as Data. My starting point is that epistemological discussions arise in any field that deals with data. I am interested in how we come to know things about theatre and about which roles data might play in this process. I systematically apply my proposed vocabulary to describe a wide range of projects from around the world as data-driven and data-assisted theater research. I complement this “guided tour” with “short excursions” into my own computational work on Indonesian and Singaporean theater. 

While some of the reflections in the book are very specific to the performing arts, these ideas might also be of interest to other people working in the computational humanities or in data science more broadly. After all, questions about context, ethics, methods, and knowledge are central to any attempt to know the world through data. Modeling a performance as data is tricky – but the same can be said for any other human activity. 

Ultimately, my goal is to show that computational research has limited, but important, applications for theater studies. We can learn how theater criticism changes over time by looking at trends in word usage. We can better grasp how theater companies grow by modeling collaborations as networks. Using geospatial visualizations, we can detect unexpected geographical “hotspots” of performances, and video processing reveals surprising patterns in the movement of actors on stage. Current research at the confluence of theater and computation is scratching the surface of what is possible, with many tantalizing possibilities just within our reach.

Find a free to read, digital copy of the book here or purchase a copy here.

REFERENCES

Holledge, Julie, Jonathan Bollen, Frode Helland, and Joanne Tompkins. 2016. A Global Doll’s House: Ibsen and Distant Visions. Palgrave Studies in Performance and Technology. London: Palgrave Macmillan.

Hechtl, Angelika, Frank Fischer, Anika Schultz, Christopher Kittel, Elisa BesheroBondar, Steffen Martus, Peer Trilcke, Jana Wolf, Ingo Börner, and Daniil Skorinkin. 2018. “Brecht Beats Shakespeare! A Card-Game Intervention Revolving Around the Network Analysis of European Drama.” In Digital Humanities 2018 Conference Abstracts, 595–96. Mexico City: ADHO.

Comments on this entry are closed.

{ 1 trackback }

Previous post:

Next post: