Valentin Gold

ConToVi: Multi-Party Conversation Exploration using Topic-Space Views

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.

Exploratory Text Analysis using Lexical Episode Plots

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.

Visual linguistic analysis of political discussions: Measuring deliberative quality

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.

Partitioning Ethnic Groups and their Members: Explaining Variations in Satisfaction with Democracy in Africa

Abstract This article examines the conditions that influence citizens’ satisfaction with democracy in Africa. In the analysis, individual, ethnic group, and national context determinants are combined in a multilevel model allowing a comparative analysis over time, countries, ethnic groups, and individuals. Using Afrobarometer survey data along with ethnic group-level and national-level data, I show that factors shaping citizens’ satisfaction can be found on each contextual level. To a large extent, perceived economic and political inequalities between ethnic groups explain variations in citizens’ satisfaction.

Interactive Visual Analysis of Transcribed Multi-Party Discourse.

We present the first web-based Visual Analytics framework for the analysis of multi-party discourse data using verbatim text transcripts. Our framework supports a broad range of server-based processing steps, ranging from data mining and statistical analysis to deep linguistic parsing of English and German. On the client-side, browser-based Visual Analytics components enable multiple perspectives on the analyzed data. These interactive visualizations allow exploratory content analysis, argumentation pattern review and speaker interaction modeling.