Volume 43, Issue 1

In digital humanities, research insights are often achieved by the interpretation of visualizations of data, but the interpretation of visualization is not self-evident. In this paper we present a new methodology to attain interpretations of visualizations which may be accepted intersubjectively. New Visual Hermeneutics is an integrated research framework with special attention to the episte­mology of visualizations. Adapting Don Ihde’s experimental phenomenology it addresses the research process as a whole, focusing on the impact of all research decisions which result in the final visualization(s) in four distinct steps: 1. data sampling, 2. algorithmic analysis of the sampled data, 3. choosing visualization of the algorithmic analysis, 4. hermeneutic interpretation of the chosen visualization.

Text mining methods applied in social sciences research are based on the premise that the analysis of texts leads to valid conclusions about social reality. The paper examines the theoretical and empirical aspects of this premise: first, it shows by recourse to the tradition of hermeneutics and the sociology of knowledge, how the relationship between text and social reality can be determined. Subsequently, it illustrates the heuristic performance of various text mining methods, using the bigram ‚social market economy‘ (ger. ‚Soziale Marktwirtschaft‘) as an example. The conclusion leads to the important analytical finding that only the constant procedural entanglement of Close and Distant Reading, referred to, in accordance with Moretti’s terminology, as Blended Reading, will allow the researcher to produce viable knowledge.

Linguistic annotation helps corpus users to retrieve relevant examples in an efficient way. It also supports the identification of latent patterns in the data by encoding generalizations such as parts of speech. Since manual annotation is very time-consuming, many projects use off-the-shelf tools for annotating or at least preprocessing their data. This article discusses pros and cons of such automatic annotation using part-of-speech tagging as an example case. It argues that errors made by annotation tools are systematic in nature and hence predictable to a certain extent. In addition, the article addresses the issue of descriptive adequacy of tagsets. In particular, it discusses how well the Stuttgart-Tübingen Tagset (STTS) describes German parts of speech. Finally, the article briefly addresses normalization, an additional preprocessing step that is sometimes required before automatic annotation tools can be applied.

The paper looks into the practice of a computer-assisted discourse analysis. Centring on the decisions and procedures that go into reconstructing multimodal frames from transmedia discourse, the paper has two aims. For one, it discusses the chances and challenges automatic text analysis has to address in facing the vast amounts of multimodal discourse that emerge in convergent media. Building on that, the paper explains the methodological premises and methodical procedures of a discourse analysis employing the computer-assisted qualitative data analysis software Atlas.ti. As such, it presents an investigation that does not answer the current prevalence of available ‘big data’ with computer power but explores the interplay between an interpretative analysis and technological support. It does so by using material from the discourse on the so-called Handygate affair. There, state authorities collected mobile phone data during the commemorative events of the Dresden bombings in February 2011. They thus created a big data collection which became the subject of public attention and was either framed as illegal and extensive instrument of state surveillance or as an efficient and accurate tool for law enforcement and targeted prosecution.