Automated strategic visualisations and user confidence

Abstract

Data visualisations aim at providing accessible and interpretable information for people. At a strategic level, such representations can be used to stimulate decision making. We have found that users are however hesitant to exploit unfamiliar visualisations, and require more material to be confident about their description of an unbiased representation of data. In this thesis we aim at exploring which characteristics affect users’ confidence in their ability to interpret and explain Topic Maps. These visualisations display the multi-dimensional thematic abstraction of large document collections, and as such require an automated generation process. In three qualitative studies, we challenge participants’ confidence with stimuli and scenarios, and analyse their responses. The studies focus on: Explanation Systems, Topic Map layouts, and mapping processes. In our studies, we demonstrate that the use of data-driven and interactive Explanation Systems gives users a sense of control, allowing for an enhanced interpretability and confidence. We then found that structure and narrative are both equally important characteristics of layouts for a confident presentation of Topic Maps. We finally explore mapping processes in detail, and establish that constructive mapping methods are more fit to improve user confidence than reductive ones. This thesis, in summary, defines a comprehensive understanding of user confidence in automatically generated visualisations.