+Data visualizations are a powerful medium for communicating complex information. Although previously mostly used in professional settings or broadcasted by the government or mainstream media, visualizations now play an increasingly prominent role in public discourse. Visualizations are central to online community building and sensemaking during developing events, such as a global pandemic. Yet when shared outside expert contexts---especially on social media---visualizations are frequently misinterpreted or even used to support harmful misleading claims. This dissertation investigates how lay audiences interpret, repurpose, and critique argumentative visualizations in online settings. Through a series of empirical studies, this dissertation explores the dynamics of data discourse on social media, analyzing both how misleading visualizations are originally presented and how communities respond to them and attempt to correct them. Drawing from the results of the analysis, I design and evaluate a set of interventions aimed at making visualizations more resilient to misinterpretation. This dissertation provides new insights into the life cycle of visualizations in public spaces and offers practical guidance for designing visualizations that anticipate and account for audience reasoning.
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