The approach regarding the “Visual Comparison of Networks”, that occupational group of visualization experts describe in the section Tools and Demos, was accepted for presentation at the PacificVis conference (April 23-26 in Bangkok).
This approach allows for the analysis of the evolution that a narrative text’s characters and their relationships take over the course of a storyline. To this end, a series of graphs that represent the character constellations in several different passages of the text can be displayed in several visual forms and thus be compared with each other. The text passages in turn are interleaved with these visualizations, which allows for the consideration of the characters’ denominations within the respective immediate context. The characters’ relationships can be further characterized by means of a summary of this context. In the case of large and multiply interconnected character sets, analysts can interact with the visualizations in order to filter and focus the elements of the graph in such a way, that the partial structures that are of interest become evident.
In two usage scenarios, it was demonstrated how a literature scholar might tackle a series of typical analysis tasks by means of the approach at hand. The scenarios were based on the one hand on a novel in modern English that had been enriched with automatically extracted character annotations, and on the other hand on a Middle High German text in which the characters had been annotated manually. The tasks demonstrated by the example of these texts comprised:
Correction of mistakes resulting from the automatic character extraction.
Quick apprehension of a character’s characteristics and of its function in the storyline’s complex of actions.
Identification of groups of characters that appear predominantly within one of the selected passages and of central “bridge characters” who connect these groups.
Characterization of relationships that central characters maintain with the others.
Verification of the hypothesis that the character graph changes substantially over the course of a series of text passages.
Verification of the hypothesis that these successive constellations are only interconnected by a few central characters.
The article by Markus John, Martin Baumann, David Schuetz, Steffen Koch, and Thomas Ertl will appear at PacificVis titled „A Visual Approach for the Comparative Analysis of Character Networks in Narrative Texts“.
In the special issue “Digital Mediävistik” of the journal “Das Mittelalter. Perspektiven mediävistischer Forschung”, an article about Social Network Analysis of Middle High German Arthurian romances will soon be published. The article addresses the question of the relationship between fairy tales and Arthurian romances, but aims to systematically and methodically redefine it. For this purpose, we firstly identify properties of the European folktale, which we, secondly, operationalize for the computational analysis and apply to a text corpus consisting of classic Arthurian romances (Hartmann’s von Aue: ‚Erec‘, and ‚Iwein’, Wolfram’s von Eschenbach ‚Parzival‘).
The investigation is carried out using data-driven methods, primarily the Social Network Analysis, and focusses on various aspects of the characters. In this way, we gain a differentiated understanding of the relation between Arthurian romances and the ‘simple form’ of fairy tales on the one hand, and the differences within the selected texts on the other hand. We show that the complex results of the statistical analyses refuse clear interpretations and thus provide new insights into the well-known objects.
The special issue “Digitale Mediävistik” including this article by Manuel Braun and Nora Ketschik will be published presumably in June 2019.
The proposal for an extension of CRETA was recently accepted. From January, the Center for Reflected Text Analytics will be funded for 2 more years by the German Federal Ministry of Education and Research.
The Social Science working unit recently published a new methodological article on the analysis of complex theoretical concepts via the use of corpus analytic and computational linguistic methods.
We identify three fundamental challenges impeding the methodological quality and long-term reputation of these promising new technologies. First, generating and pre-processing very large text corpora is still a laborious and costly enterprise. Secondly, Social Scientists want to learn from text about societal context and reconstruct meaning along the lines of complex theoretical concepts. The semantically valid operationalization of complex social-scientific concepts, however, remains a problem. Thirdly, scholars need flexible data output and visualization options to connect the data generated by corpus-linguistic methods with the discipline’s existing research. Many tools designed for linguistic research questions do not provide options suitable for social scientific research. We will conclude that it is possible to solve these problems; however, hermeneutically sensitive uses of computer-linguistic methods will take much more time, work and creativity than often assumed. Moreover, there can be no one-size-fits-all solutions to these problems. Social scientists need and want to decide upon methodological questions in the light of their oftentimes highly specific research questions. The process of reflectively appropriating big-data methods in the Social Sciences has only just begun.