Quantitative Drama Analytics: Tracking Character Knowledge (Q:TRACK)

Nils Reiter & Marcus Willand

Q:TRACK’s research programme interlinks objectives in two research areas: literary and drama history and computational literary studies. In the first area, we are aiming at new insights about the way social knowledge is used to drive or solve the dramatic conflict in plays. We initially focus on the distribution and dissemination of knowledge within the single dramatic world of a play. An outcome of our work programme will be a formal model of the knowledge that each character as well as the audience has and gains within each segment (act, scene, appearance). Such a model, once it has been established, allows to visualize relations as well as drawing inferences. This can support close reading, but also facilitates comparing plays in a systematic way. Once such models can be created automatically, we will “zoom out” and look at dissemination patterns from a drama historical point of view, asking how they change over the centuries. A dissemination pattern is a generalization of the model found within a set of plays that is achieved by disregarding character names and allowing for some variation in the details. One such pattern might be that the relation triggering the anagnorisis is transmitted via a character not involved in the actual relation (as heralds, servants etc.). In order to pursue our literary and drama historic goals, we thus systematically reconstruct constellations, modes and techniques of dramatic knowledge distribution. As an outcome of this project, we will gain insights into the blueprints of the art of playwriting. By taking a multi-perspectival viewpoint on character and audience knowledge we not only assure ourselves of a non-interpretive access to the plays. We also provide an intersubjective, meta-historical approach to the identification of text elements that supposedly function as sympathy and empathy control of readers and trigger dramatic effects as the purgation of emotions (catharsis).

URL: quadrama.github.io