DHN 2018 Workshop: Network Analysis of Literary Texts - Operationalisation, Visualisation, Interpretation

Frank Fischer (Higher School of Economics, Moscow & DARIAH-EU)

Danil Skorinkin (Higher School of Economics, Moscow)

Network analysis can be be applied to many kinds of data sets. In the humanities, the methods of network theory are used to process network data in the fields of history, linguistics, or literature. While underlying methods and algorithms are a connecting element, the pre-processing and analysis of data may differ significantly in different disciplines.

This DARIAH-EU-sponsored workshop concentrates on social networks extracted from fictional texts, dramatic texts in particular. There are many ways to implement such thing, depending on the kind of research question. This 0,5-day workshop will address the operationalisation of such an approach.

The workshop starts with a historical contextualisation, summing up 20 years of attempts to extract and analyse character networks from fictional texts. Basic measures of network analysis are introduced, such as network density, average path length, and betweenness centrality, before quickly proceeding to the hands-on session, where we will learn how to acquire and generate network data from literary texts. The next step involves the visualisation and analysis of extracted network data, leading to a discussion on how to interpret this kind of data in the context of fictional texts.

No previous knowledge is required to take part in the workshop. The hands-on part will mainly focus on dramatic texts, but we will also discuss methods for the extraction of character networks from prose. Software we will use comprise our own tool "ezlinavis" (https://ezlinavis.dracor.org/) and Gephi (https://gephi.org/), as well as some simple scripting.

At the end of the workshop you should be able to visualise and analyse network data like this, extracted from Aleksis Kivi's play "Nummisuutarit" ("Heath Cobblers") from 1864:

In preparation, participants can have a look at our research blog (https://dlina.github.io/) and toy around with the example data provided in "ezlinavis" (example button in the right upper corner). Feel free to reach out to us for any questions you may have.

Maximum number of participants: 20.

(The "Nummisuutarit" network graph was done by Frank Fischer in December 2017 and is licensed under CC BY 4.0.)