title: Understanding visualization discourse and building discursive visualizations
Creators: name: Kauer, Tobias
Publisher: name: The University of Edinburgh
Year: 2025
Subjects: subject: Discursive Visualization
subject: Audience Annotations
subject: Collective Storytelling
subject: Collaborative Sensemaking
subject: Anchored Annotations

description: This doctoral thesis investigates the potential of discursive visualizations to entice readers of visualizations to discussions about data. Through a suite of surveys, interfaces, and user studies it explores key aspects of how people talk about data and demonstrates how these conversations can be integrated into visualizations. Discursive visualization describes a genre of visualization interfaces, engagement practices, and representation methods that aim to integrate discussions about visualizations into visualizations. Data visualization has become a popular, ubiquitous medium in science, journalism, entertainment, and across a variety of online environments, including news outlets, social media, dedicated blogs, or expert groups. When discussing data visualizations, expert and novel audiences alike express their thoughts and observations about the data. Existing annotation systems have explored various facets and scenarios of these discussions about data visualizations, including collaborative sensemaking, public communication, or participatory data correction. However, considering the ongoing increase in potential audiences, use cases, and venues to encounter and talk about data visualizations, the potential of understanding discourse and the interfaces required for its integration remain under-explored. To explore the potential of existing discussions about data, I provide a structured analysis of public audience reactions to visualizations observed outside of controlled lab environments. My results describe ten reaction types on four reaction scopes as well as a description of intrinsic and extrinsic engagement drivers that motivate people to participate in discussions. Building on this knowledge, I design an interactive prototype that allows to annotate data visualizations with personal stories. Small but important details, for example where an annotation is placed or who write it are generally overlooked in prior research. My interface offers different viewing conditions to better understand the impact of such details on the reading experience, particularly focusing on the role of embedded audience annotations. My results inform the notion of 'collective storytelling' that describes a genre of visualizations providing a space in which audience annotations become the primary concern of visualizations. In a follow-up study, I design another prototype that introduces 'anchored annotations' and 'discursive patinas' as core mechanics to integrate discussions into visualizations, regardless of their specific chart types, topics, or desired reaction types. My evaluation shows that discursive patinas improve the ability to navigate discussions and guide people to comments that help to understand, contextualize, or scrutinize the visualization. My studies demonstrate how discursive visualization supports collaborative sensemaking and allows for novel engagements with data and data visualizations, including collective storytelling, distant readings of discussions, and close readings of visualizations. Audience annotations are usually not required in visualization design. Yet, they are practical devices that are at the core of discursive engagements, as they allow to guide interpretation, offer contrasting perspectives, offer feedback, establish personal relevance, promote empathy for others, and help people reflect on their own experiences. Conceding space and visual presence to the audience challenges existing power dynamics in the relation between visualization creators and consumers. Offering a platform for discussions that are not considered when designing visualizations allows for a multiplicity of narratives to coexist. Integrating local knowledge into visualizations can point to missing, biased, or simply incorrect data, and improve the validity of the data as a whole. Based on these considerations, I see great potential for discursive visualization to provide the foundation for polyvocal, personal, and critical engagements with data.