Organised by Research Area 5: "Building Digital Communities".

Presentation by Miguel Escobar Varela

Computational methods can better help us understand the history and current landscape of the performing arts. For example, we can use network analysis and simulations to study how collaborations within theatre companies develop over time, and how specific management decisions lead to different collaborative patterns. For this, we can take advantage of the records of theatre productions, which are increasingly available in digitised form.

Recent advances in open-soucre AI models can also be used to extract detailed information from videos and texts. For example, we can fine-tune action segmentation models to identify culturally-specific performing conventions in video recordings of performances, and determine how their usage has changed over time. These methods might not be useful for all projects, and approaches such as ethnography and practice-based research might be better suited for certain research questions. However, in cases where scale matters and when large datasets are available, computational methods can offer unique perspectives on the performing arts that can complement other approaches.