• El-Assady, Mennatallah Valentin Gold, Carmela Acevedo, Christopher Collins, and Daniel Keim. 2016. ConToVi: Multi-Party Conversation Exploration using Topic-Space ViewsComputer Graphics Forum, vol. 35, no. 3, pp. 431-440. [pdf]/[video]

    We introduce a novel visual analytics approach to analyze speaker behavior patterns in multi-party conversations. We propose Topic-Space Views to track the movement of speakers across the thematic landscape of a conversation. Our tool is designed to assist political science scholars in exploring the dynamics of a conversation over time to generate and prove hypotheses about speaker interactions and behavior patterns. Moreover, we introduce a glyph-based representation for each speaker turn based on linguistic and statistical cues to abstract relevant text features. We present animated views for exploring the general behavior and interactions of speakers over time and interactive steady visualizations for the detailed analysis of a selection of speakers. Using a visual sedimentation metaphor we enable the analysts to track subtle changes in the flow of a conversation over time while keeping an overview of all past speaker turns. We evaluate our approach on real-world datasets and the results have been insightful to our domain experts.
  • Gold, Valentin, Annette Hautli-Janisz, and Katharina Holzinger. 2016. VisArgue - Analyse von politischen Verhandlungen. Zeitschrift für Konfliktmanagement, vol. 3, no. 16, pp. 98-99. 

    Politikwissenschaftler, Linguisten und Informatiker der Universität Konstanz haben interdisziplinär drei Jahre lang im Rahmen eines Projekts ``VisArgue`` eine Software entwickelt, die politische Kommunikation analysiert, visualisiert und Rückschlüsse auf deren Effektivität zulässt. Erprobt wurden die Tools am Beispiel von Stuttgart 21.


  • Gold, Valentin, Mennatalla El-Assady, Tina Bögel, Christian Rohrdantz, Miriam Butt, Katharina Holzinger and Daniel Keim. 2015. Visual Linguistic Analysis of Political Discussions: Measuring Deliberative Quality. Digital Scholarship in the Humanities, First published online: 10 September 2015. DOI: http://dx.doi.org/10.1093/llc/fqv033

    This article reports on a Digital Humanities research project which is concerned with the automated linguistic and visual analysis of political discourses with a particular focus on the concept of deliberative communication. According to the theory of deliberative communication as discussed within political science, political debates should be inclusive and stakeholders participating in these debates are required to justify their positions rationally and respectfully and should eventually defer to the better argument. The focus of the article is on the novel interactive visualizations that combine linguistic and statistical cues to analyze the deliberative quality of communication automatically. In particular, we quantify the degree of deliberation for four dimensions of communication: Participation, Respect, Argumentation and Justification, and Persuasiveness. Yet, these four dimensions have not been linked within a combined linguistic and visual framework, but each single dimension helps determining the degree of deliberation independently from each other. Since at its core, deliberation requires sustained and appropriate modes of communication, our main contribution is the automatic annotation and disambiguation of causal connectors and discourse particles.
  • Gold, Valentin, Christian Rohrdantz and Mennatalla El-Assady. 2015. Exploratory Text Analysis using Lexical Episode Plots. [video]. Eurographics Conference on Visualization (EuroVis) - Short Papers, The Eurographics Association. DOI: 10.2312/eurovisshort.20151130

    In this paper, we present Lexical Episode Plots, a novel automated text-mining and visual analytics approach for exploratory text analysis. In particular, we first describe an algorithm for automatically annotating text regions to examine prominent themes within natural language texts. The algorithm is based on lexical chaining to find spans of text in which the frequency of a term is significantly higher than its average in the document. In a second step we present an interactive visualization supporting the exploration and interpretation of Lexical Episodes. The visualization links higher-level thematic structures with content-level details. The methodological capabilities of our approach are illustrated by analyzing the televised US presidential election debates.


  • Oelke, Daniela, Hendrik Strobelt, Christian Rohrdantz, Iryna Gurevych and Oliver Deussen. 2014. Comparative Exploration of Document Collections: a Visual Analytics Approach. [video]. Computer Graphics Forum, vol. 33, no. 3, pp. 201-210.

    We present an analysis and visualization method for finding common and discriminative topics when comparing document collections. At first, we apply probabilistic topic modeling and examine the resulting topic set algorithmically to find topics that discriminate a subset of collections from the remaining ones. The computed discriminative topics have to be both distinctive and characteristic, which are two abstract criteria that we model algorithmically through a set of heuristics. Furthermore, we suggest a novel visualization method that we call DiTop-View, in which topics are represented by glyphs (topic coins) that are arranged meaningfully in a 2D plane. Topic coins are designed to encode all information necessary for performing comparative analyses such as the class membership of a topic, its most descriptive terms and the discriminative relations. We assess our topic analysis using statistical measures and a small user experiment. To evaluate the whole system we present an expert case study with researchers from political sciences analyzing two real-world datasets. 
  • Bögel, Tina, Annette Hautli-Janisz, Sebastian Sulger and Miriam Butt. 2014. Automatic Detection of Causal Relations in German Multilogs. In Proceedings of the EACL 2014 Workshop on Computational Approaches to Causality in Language (CAtoCL), Association for Computational Linguistics, pp. 20-27. Gothenburg, Sweden.

    This paper introduces a linguistically motivated, rule-based annotation system for causal discourse relations in transcripts of spoken multilogs in German, with the aim of providing an automatic means of determining the degree of justification provided by a speaker in the delivery of an argument in a multiparty discussion. The system comprises of two parts: A disambiguation module which differentiates causal connectors from their other senses, and a discourse relation annotation system which marks the spans of text that constitute the reason and the result/conclusion expressed by the causal relation. The system is evaluated against a gold standard of German transcribed spoken dialogue. The results show that our system performs reliably well with respect to both tasks.


  • Andreas Lamprecht, Annette Hautli, Christian Rohrdantz, Tina Bögel. 2013. A Visual Analytics System for Cluster Exploration. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pages 109–114, Sofia, Bulgaria.

    This paper offers a new way of representing the results of automatic clustering algorithms by employing a Visual Analytics system which maps members of a cluster and their distance to each other onto a twodimensional space. A case study on Urdu complex predicates shows that the system allows for an appropriate investigation of linguistically motivated data.