We present the first dataset, an annotation scheme, discourse analysis, and baseline experiments on argumentation and domain content types in scholarly articles on political science, specifically on the theory of International Relations (IR). The dataset comprises over 1 600 sentences stemming from three foundational articles on Neo-Realism, Liberalism, and Constructivism. We show that our annotation scheme enables educationally-relevant insight into the scholarly IR discourse and that state-of-the-art classifiers, while effective in distinguishing basic argumentative elements (Claims and Support/Attack relations) reaching up to 0.97 micro F1 , require domain-specific training and fine-tuning on the more fine-grained tasks of relation and content type prediction.

The lack of visually guided data exploration tools limits the scope of research questions communication scientists are able to study. The Comment Analyzer steps in where traditional statistical tools fail when it comes to researching the commenting behavior of news article readers. The basis of such an analysis are comment-thread corpora in which comments are tagged with various deliberative quality indicators as well as political stance. Our analysis tool provides a visual querying system for the exploration and analysis of such corpora and allows social scientists to gain insights into the distributions and relations between comment attributes, the homogeneity of thread sets, frequent thread structures and changes in comment qualities over the course of a single but in particular of multiple threads at once. We developed the tool in close collaboration with communication scientists in a user-centered approach. The system has proven its utility in thorough reviews with the communication scientists, by corroborating existing findings in the literature but particularly by provoking and answering new research questions. Final reviews with five independent experts confirmed these observations and revealed the potential of the Comment Analyzer for other datasets currently being created and analyzed in the communication sciences.

Abstract This paper presents new techniques for seamlessly transitioning between parallel coordinate plots, star plots, and scatter plots. The star plot serves as a mediator visualization between parallel coordinate plots and scatter plots since it uses lines to represent data items as parallel coordinates do and can arrange axes orthogonally as used for scatter plots. The design of the transitions also motivated a new variant of the star plot, the polycurve star plot, that uses curved lines instead of straight ones and has advantages both in terms of space utilization and the detection of clusters. Furthermore, we developed a geometrically motivated method to embed scatter points from a scatter plot into star plots and parallel coordinate plots to track the transition of structural information such as clusters and correlations between the different plot types. The integration of our techniques into an interactive analysis tool for exploring multivariate data demonstrates the advantages and utility of our approach over a multi‐view approach for scatter plots and parallel coordinate plots, which we confirmed in a user study and concrete usage scenarios.

This paper presents a visual analytics system for exploring, analyzing and comparing argument structures in essay corpora. We provide an overview of the corpus by a list of ArguLines which represent the argument units of each essay by a sequence of glyphs. Each glyph encodes the stance, the depth and the relative position of an argument unit. The overview can be ordered in various ways to reveal patterns and outliers. Subsets of essays can be selected and analyzed in detail using the Argument Unit Occurrence Tree which aggregates the argument structures using hierarchical histograms. This hierarchical view facilitates the estimation of statistics and trends concerning the progression of the argumentation in the essays. It also provides insights into the commonalities and differences between selected subsets. The text view is the necessary textual basis to verify conclusions from the other views and the annotation process. Linking the views and interaction techniques for visual filtering, studying the evolution of stance within a subset of essays and scrutinizing the order of argumentative units enable a deep analysis of essay corpora. Our expert reviews confirmed the utility of the system and revealed detailed and previously unknown information about the argumentation in our sample corpus.

This paper presents a visualization framework that aids readers in understanding and analyzing the contents of medium-sized text collections that are typical for the opus of a single or few authors. We contribute several document-based visualization techniques to facilitate the exploration of the work of the German author Bazon Brock by depicting various aspects of its texts, such as the TextGenetics that shows the structure of the collection along with its chronology. The ConceptCircuit augments the TextGenetics with entities - persons and locations that were crucial to his work. All visualizations are sensitive to a wildcard-based phrase search that allows complex requests towards the author's work. Further development, as well as expert reviews and discussions with the author Bazon Brock, focused on the assessment and comparison of visualizations based on automatic topic extraction against ones that are based on expert knowledge.

Several approaches have been proposed to model either the explicit sequential structure of an argumentative text or its implicit hierarchical structure.So far, the adequacy of these models of overall argumentation remains unclear.This paper asks what type of structure is actually important to tackle downstream tasks in computational argumentation.We analyze patterns in the overall argumentation of texts from three corpora.Then, we adapt the idea of positional tree kernels in order to capture sequential and hierarchical argumentative structure together for the first time.In systematic experiments for three text classification tasks, we find strong evidence for the impact of both types of structure.Our results suggest that either of them is necessary while their combination may be beneficial.