Disclose to Tell: a Data Design Framework for Alternative Narratives

Abstract

Data visualization for alternative narratives is a powerful adversarial tool that seeks to inquire about the existing conditions of conflicts. Nowadays, data visualization is widely used in activist communication to offer data-driven counternarratives to those with dominant power. However, a study of 64 cases shows that most visualizations are far from following the open-source ethos that data activism advocates. If visualizations for alternative narratives do not open up their processes and communicate how the meaning of data is shaped and translated visually, they risk turning into black-box devices that crystallize biased representations of conflict rather than investigating them. This paper presents a data design framework for visualizations used in alternative narratives. It is presented as a methodological tool that encourages actionable data practices that promote a more critical and reflective data culture. The framework is structured in two ways of approaching the process of working with data: from the parts of the process and from the process as a whole. The first approach presents four lenses intended to materialize aspects of the process of working with and making sense of data: Open/close, Composition, Zoom, and Sanitization. The second approach proposes a self-hacking operation of disclosing the production process of the visualizations. This paper argues that visualizations for alternative narratives ought to be open artifacts that promote the democratization of re-interpretation and critical data by disclosing the design decisions that were made on the dataset, as well as its visual, and interactive representation.