VIANA: Visual Interactive Annotation of Argumentation Argumentation Mining addresses the challenging tasks of identifying boundaries of argumentative text fragments and extracting their relationships. Fully automated solutions do not reach satisfactory accuracy due to their insufficient incorporation of semantics and domain knowledge. Therefore, experts currently rely on timeconsuming manual annotations. In this paper, we present a visual analytics system that augments the manual annotation process by automatically suggesting which text fragments to annotate next. The accuracy of those suggestions is improved over time by incorporating linguistic knowledge and language modeling to learn a measure of argument similarity from user interactions. Based on a longterm collaboration with domain experts, we identify and model five highlevel analysis tasks. We enable close reading and notetaking, annotation of arguments, argument reconstruction, extraction of argument relations, and exploration of argument graphs. To avoid context switches, we transition between all views through seamless morphing, visually anchoring all text and graphbased layers. We evaluate our system with a twostage expert user study based on a corpus of presidential debates. The results show that experts prefer our system over existing solutions due to the speedup provided by the automatic suggestions and the tight integration between text and graph views.

VISTX: Experimenting with Interactive Stance and Sentiment Visualisation Usergenerated content online continues to grow rapidly, and with this it becomes more challenging to evaluate the general consensus on a trending topic. Even greater challenges are faced if one wishes to compare opinions across multiple topics, as it can be difficult to make comparisons and reach conclusions with large amounts of data in an intuitive manner. VISTX explores innovative methods of visualising opinion in terms of stance and sentiment using Twitter data from the SemEval2016 Stance Dataset and presents three types of interactive visualisations. We discuss the method of testing the visualisations for effectiveness with a small group of users, where time taken and the number of incorrect interpretations were measured for each user. We discuss the results, which indicate that the xe2x80x98circlepackingxe2x80x99 visualisation type may lead to quicker interpretations for five or more topics. Finally, we discuss the work needed to further solidify these initial findings.