Digitale Netzwerkanalyse dramatischer Texte

Trilcke Peer; Fischer Frank; Kampkaspar Dario

Book chapter

Digitale Netzwerkanalyse dramatischer Texte

Trilcke P., Fischer F., Kampkaspar D.

Das Projekt ‘Digitale Netzwerkanalyse dramatischer Texte’ steht in der Tradition strukturanalytischer Ans¨atze in der Literaturwissenschaft (allgemein Titzmann 1977), die es einerseits im Sinne eines konsequent netzwerkanalytischen Relationismus (mit Rekurs auf die Social Network Analysis, siehe u. a. Wasserman/Faust 1998), andererseits unterstutzt durch Verfahren der automatisierten ¨ Datenerhebung und -auswertung weiterentwickelt, um sie auf gr¨oßere Textkorpora anzuwenden und so umfassende relationale Daten uber Prozesse des literaturgeschichtlichen Strukturwandels ¨ gewinnen zu k¨onnen.

Language: German

Text on another site

Keywords: Network Analysis

In book

DHd2015. Von Daten zu Erkenntnissen 23. bis 27. Februar 2015, Graz. Book of Abstracts

Austrian Centre for Digital Humanities, 2015.

Digital Network Analysis of Dramatic Texts

Fischer F., Göbel M., Kampkaspar D. et al. Proceedings of DH2015. 2015.

Added: Sep 28, 2016

Literaturwissenschaft als Hackathon. Zur Praxeologie der Digital Literary Studies und ihren epistemischen Dingen

Trilcke P., Fischer F. Zeitschrift für digitale Geisteswissenschaften. 2018. Vol. Sonderband 3.

This essay questions whether digital literary studies can still be meaningfully regarded as part of literary studies. This heretical question is motivated by a praxeological view of a research project for the network analysis of dramatic texts, in particular by reflecting on the project’s underlying ›epistemic thing‹, which in this case consists of specifically-formatted structural data (and not the actual primary texts themselves). What does this corpus of structural data, which was extracted from 465 plays spanning the period from 1730 to 1930, have to do with the ›epistemic things‹ of literary studies? We explore this question by providing insight into our analyses, which describe the structural evolution of the ›plays‹, try to locate ›small world‹ properties in our corpus, and develop new metrics for plot analysis. The results show not only how digital methods can supplement or enrich literary studies; they also raise questions about how digital the field of literary studies already is, since its research objects are increasingly available in digital forms.

Added: Aug 13, 2018

Introducing RusDraCor – A TEI-Encoded Russian Drama Corpus for the Digital Literary Studies

Fischer F., Orlova T., Skorinkin D. et al. In bk.: Corpus Linguistics 2017 Abstracts. 2017. P. 28-31.

We describe the creation of a corpus of Russian-language drama, comprising hundreds of texts from the middle of the 18th century to the first third of the 20th century. Texts are encoded in the XML-based markup standard TEI, the focus is on extra-linguistic, structural annotations, although additional annotation layers can be added easily.

Added: Jun 28, 2017

Network Dynamics, Plot Analysis: Approaching the Progressive Structuration of Literary Texts

Fischer F., Göbel M., Kampkaspar D. et al. In bk.: Digital Humanities 2017 (Montréal, 8–11 August 2017). Book of Abstracts. Montréal: McGill University, 2017.

Added: Aug 8, 2017

Le drame comme réseau de relations. Une application de l’analyse automatisée pour l’histoire littéraire du théâtre

Fischer F., Dazord G., Göbel M. et al. Revue d'Historiographie du Théâtre. 2017. Vol. 4. P. 1-9.

Of late, the network analysis of literary texts has grown into an independent research field of digital literary studies. Since analysing the network structure of unique texts promises just marginal results, the perspective should shift towards a ‘distant reading’ of hundreds or thousands of texts. In this paper, we describe how the process of formalising literary data is facilitated by machine-readable corpora comprising hundreds of dramatic texts in several languages. Taking a corpus of roughly 500 German-language dramas as an example, we demonstrate how the calculation of network metrics and visualisations can deliver new material for interpretation and offer new insights into the evolution of drama.

Added: Dec 27, 2017

Network analysis of international migration

Aleskerov F. T., Meshcheryakova N. G., Rezyapova A. et al. Математические методы анализа решений в экономике, бизнесе и политике. WP7. Издательский дом ВШЭ, 2016. No. WP7/2016/06.

The paper analyses international migration flows from the network perspective by the evaluation of centrality indices. In order to find the most influential countries in the international migration network classical centrality indices and new centrality indices are evaluated. New centrality indices consider short (SRIC) and long-range (LRIC) indirect interactions and the node attribute – population of the destination country. The model is applied to the annual data on international migration flows from 1970 to 2013 provided by United Nations Organization. The analysis is made for one year of each decade and indices’ dynamics is described. It is shown that countries with huge migration flows are outlined by both classical and SRIC, LRIC indices, and SRIC and LRIC indices point out countries with considerable outflows of migrants to countries highly involved in international migration and the most interconnected countries.

Added: Nov 2, 2016

Netzwerkdynamik, Plotanalyse – Zur Visualisierung und Berechnung der 'progressiven Strukturierung' literarischer Texte

Trilcke Peer, Fischer Frank, Göbel M. et al. In bk.: DHd2017. Digitale Nachhaltigkeit. 13. bis 18. Februar 2017, Bern. Book of Abstracts. Bern: Universität Bern, 2017. P. 175-180.

Added: Jul 25, 2017

Computational Aspects and Applications in Large-Scale Networks. Springer Proceedings in Mathematics & Statistics

Valery A. Kalyagin, Panos M. Pardalos, Oleg Prokopyev et al. Vol. 247. Springer, 2018.

Contributions in this volume focus on computationally efficient algorithms and rigorous mathematical theories for analyzing large-scale networks. Researchers and students in mathematics, economics, statistics, computer science and engineering will find this collection a valuable resource filled with the latest research in network analysis. Computational aspects and applications of large-scale networks in market models, neural networks, social networks, power transmission grids, maximum clique problem, telecommunication networks, and complexity graphs are included with new tools for efficient network analysis of large-scale networks.

This proceeding is a result of the 7th International Conference in Network Analysis, held at the Higher School of Economics, Nizhny Novgorod in June 2017. The conference brought together scientists, engineers, and researchers from academia, industry, and government.

Added: Aug 30, 2018