Evolutive Dynamics in Early Modern European Drama
A Computational Approach
Luca Giovannini
(Potsdam/Padova)
Potsdam, 10.03.2025, 16:00
Campus Neues Palais, House 11, Room 2.19
PhD defense
Theory
Research question
How did the different European dramatic literatures develop their own peculiar features during the early modern era?

Corneille, Cinna (1639)

Shirley, The Gentleman of Venice (1639)
A possible explanation
According to Moretti (1994), early modern drama evolved according to the laws of biological speciation — with dramatic forms moving into new geographic spaces and 'mutating'

1400 1500 1600 1700 1800
This is how Moretti envisions the development of European tragedy
🇫🇷
🇩🇪
🇮🇹
🇬🇧
🇪🇸
Common model of European drama (based on Seneca and the medieval heritage)
Concurring explanations
- Emphasising unity over diversity:
- Küpper (2018): early modern drama as a cultural
net in which textual elements such as plots, characters, and motifs circulate and are periodically extracted, reworked and reused in individual
plays - Clubb (1990): diffusion of theathergrams
- Küpper (2018): early modern drama as a cultural
How to empirically investigate the evolution of early modern drama?
Corpus
Corpus features
-
150 plays in five languages (🇮🇹 🇫🇷 🇪🇸 🇩🇪 🇬🇧)
-
time span: 1561-1710 (150 years)
-
purposefully non-canonical approach
-
balance between representativeness and practicability

Birth locations for the corpus authors (via Wikidata, partial data)

Text onboarding pipeline


Homepage
Methods
Operationalising drama
- Identifying key component of dramatic texts:
- dialogue
- characters
- plot
- Finding suitable methods to capture them:
- a mix of quantitative formalist approaches
- content- and language-agnostic
- form-oriented
- inspiration: Boris Yarkho et al.
- a mix of quantitative formalist approaches
Collecting drama metrics
via the DraCor API or re-computed independently
(following Algee-Hewitt 2017, Szemes and Vida 2024, Trilcke et al. 2017)
Type | Features |
---|---|
Network | Size; Average clustering coefficient; Density; Average path length; Average degree; Diameter; Maximum degree; Number of edges; Number of connected components; Ratio, average degree to maximum degree; Ratio, maximum degree to number of characters; Weighted degree distribution; Protagonism; Mediateness |
Cast and speech | Average characters per scene; Average length of character speech; Speech intensity; Gendered speakers; Collective speakers |
Size | Number of acts; Number of Segments; Word count, whole text; Word count, spoken text; Word count, stage directions; Number of prose lines; Number of verse lines |
Plot | All-in index; Final scene size; Drama change rate |
Desiderata
- go beyond the comparison of single measures
- capture different structural dimensions of
drama in an holistic fashion
Solution
- collect metrics in feature vectors or play embeddings
- containing a variety of measures embodying different textual aspects of the dramatic text
example_play_1 = {10, 2, 0.5714, 3, 16792, ...}
example_play_2 = {26, 6, 0.3422, 2, 40098, ...}
Experiments
Basic hypothesis
- distances between vectors can be assumed to express the degree of (dis)similarity between them
- two plays whose embeddings are quite far one from the other will also be different in terms of form
- the branching of dramatic traditions can be seen through the ’progressive distancing’ of the plays’ vectors
1. Distances
Two options for measuring distance between vectors

pairwise
(cf. Wittgenstein's family resemblances)
centroid-based
(cf. ideal type theory)
Pairwise implementation


Centroid-based implementation
2. Clusters
1. Representing play vectors as points on a Cartesian plane via dimensionality reduction methods (here: PCA)
2. Visually identifying clusters based on formal/structural similarities


Some clustering seems to emerge towards the end, but it's still not enough
3. Patterns
Approach
- Focus on each individual metric and follow its evolution across the different sub-corpora by computing shifts in absolute value across the time span
- Verify whether the variation of a given feature has become highly distinctive of a national tradition against the others
- Caveat: not all metric variations are significant!

First steps towards a quantitative profiling of dramatic traditions

Types of measure: network - size - cast & speech - plot
A reproduction experiment
- Data: EngDraCor + FreDraCor, 1561-1710
- Method: vectorisation and distance experiments
- Goal: compare results to those obtained in the corresponding EmDraCor subsets
-
Results:
- patterns recognised in the smaller EmDraCor sub-corpora are amplified versions of those found in the broader ones
- results from EmDraCor are not fully replicated, but they can still highlight trends in the formal development of European theatre

A vindication of vectorisation
Conclusions
- (teleological) theoretical framework
- other models of cultural evolution (see Sobchuk 2023) might be more suitable to interpret drama evolution
- corpus specifics (size, composition, etc.)
- operationalisation of the concept of drama
- choice of metrics employed!
Limitations
- construction of a multilingual, open-access, machine-actionable corpus of
150 TEI/XML-encoded plays - empirical reassessment of a previous theory via quantitative methods ('triangulation')
- further development of a key methodology (vectorisation of text based on formal features)
Contributions
Bibliography
-
Algee-Hewitt, M. (2017). "Distributed character: Quantitative models of the English stage, 1550–1900". New Literary History, 48(4), 751-782.
-
Allison, S.; Heuser, R.; Jockers, M.; Moretti, F.; Witmore, M. (2011). "Quantitative Formalism: An Experiment". Stanford Literary Lab Pamphlet 1.
-
Clubb, L. G. (1990). Italian Drama in Shakespeare’s Time. New Haven: Yale UP.
-
Fischer, F., Börner, I.; Göbel, M.; Hechtl, A.; Kittel, C.; Milling, C., Trilcke, P. (2019). "Programmable Corpora: Introducing DraCor, an Infrastructure for the Research on European Drama". In: DH2019 Book of Abstracts. University of Utrecht, 2019.
-
Jannidis, F. (2022). "Digitale Literaturwissenschaft. Zur Einführung". In: Jannidis, F. (ed.), Digitale Literaturwissenschaft. Stuttgart: J. B. Metzler, pp. 1-16.
-
Herrmann, J. B.; Bories, A.-S.; Frontini, F.; Jacquot, C.; Pielström, S.; Rebora, S.; Rockwell, G.; Sinclair, S. (2023). "Tool Criticism in Practice: On Methods, Tools, and Aims of Computational Literary Studies". Digital Humanities Quarterly 17, 2.
-
Krämer, S. (2023). "The Cultural Technique of Flattening". Metode 1.
-
Küpper, J. (2018). The Cultural Net: Early Modern Drama as a Paradigm. Berlin; Boston: De Gruyter.
-
Lvoff, B. (2021). "Distant Reading in Russian Formalism and Russian Formalism in Distant Reading". Russian Literature, 122, 29-65.
-
Moretti, F. (2013). Distant reading. London: Verso.
-
Schöch, C. (2023). "Repetitive research: a conceptual space and terminology of replication, reproduction, revision, reanalysis, reinvestigation and reuse in digital humanities". International Journal of Digital Humanities, 5 (2): 373–403.
-
Sobchuk, O. (2023). "Evolution of Modern Literature and Film", in J. Tehrani, J. Kendal, and R. Kendal, eds., The Oxford Handbook of Cultural Evolution (online).
-
Szemes, B., and Vida, B. (2024) "Tragic and Comical Networks: Clustering Dramatic Genres According to Structural Properties". In M. Andresen and N. Reiter, eds., Computational Drama Analysis: Reflecting on Methods and Interpretations. Berlin; Boston: De Gruyter.
-
Willand, M., Trilcke, P., Schöch, C., Rißler-Pipka, N., Reiter, N., & Fischer, F. (2017). "Aktuelle Herausforderungen der Digitalen Dramenanalyse". In DHd 2017 Book of Abstracts.
Methodological reflection
- drama vectorisation as an expression of the ’cultural technique of flattening’ (Krämer 2023) typical of DH
- not as a reductionist attempt to overtly simplify the complexity of theatrical texts
- rather as a prosecution of formalist morphological thinking with modern computational methods
- interplay between operationalisation and flattening:


'Irregular' theatres
- e.g. Spanish and English
- influenced by medieval theatre practices
- emphasis on performance
- looser structure
'Regular' theatres
- e.g. French and Italian
- associated with humanist theatre
- meant mostly to be read
- codified structure after Aristotle
Early modern drama aesthetics
example-based cultures
rule-based cultures
according to Lotman's typology of culture
- 🇬🇧 : enhanced network connectedness, dispersion of the protagonist role (cf. Algee-Hewitt 2017), increase in female cast, progressive implementation of French liaison des scènes
- 🇩🇪 : shift towards sparser networks, increase in segmentation (Neoclassical influence)
- 🇪🇸 : expanding and more intricately connected dramatic networks, absence of central mediating figures, increase in non-gendered speakers, decreasing drama change rate
-
🇮🇹 : progressive concentration of the protagonist role in
one or few characters, increase of stage directions (CdA) - 🇫🇷 : streamlined network models, more stability in stage configurations
Evolutionary trends within the subcorpora
#PhD: Defense (short)
By luca-giovannini
#PhD: Defense (short)
- 137