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The growing popularity of interactive time series exploration platforms has made data visualization more accessible to the public. However, the ease of creating polished charts with preloaded data also enables selective information presentation, often resulting in biased or misleading visualizations. Research shows that these tools have been used to spread misinformation, particularly in areas such as public health and economic policies during the COVID-19 pandemic. Post hoc fact-checking may be ineffective because it typically addresses only a portion of misleading posts and comes too late to curb the spread. In this work, we explore using visualization design to counteract cherry-picking, a common tactic in deceptive visualizations. We propose a design space of guardrails—interventions to expose cherry-picking in time-series explorers. Through three crowd-sourced experiments, we demonstrate that guardrails, particularly those superimposing data, can encourage skepticism, though with some limitations. We provide recommendations for developing more effective visualization guardrails. 

Attempting to make sense of a phenomenon or crisis, social media users often share data visualizations and interpretations that can be erroneous or misleading. Prior work has studied how data visualizations can mislead, but do misleading visualizations reach a broad social media audience? And if so, do users amplify or challenge misleading interpretations? To answer these questions, we conducted a mixed-methods analysis of the public's engagement with data visualization posts about COVID-19 on Twitter. Compared to posts with accurate visual insights, our results show that posts with misleading visualizations garner more replies in which the audiences point out nuanced fallacies and caveats in data interpretations. Based on the results of our thematic analysis of engagement, we identify and discuss important opportunities and limitations to effectively leveraging crowdsourced assessments to address data-driven misinformation. 

Data visualizations can empower an audience to make informed decisions. At the same time, deceptive representations of data can lead to inaccurate interpretations while still providing an illusion of data-driven insights. Existing research on misleading visualizations primarily focuses on examples of charts and techniques previously reported to be deceptive. These approaches do not necessarily describe how charts mislead the general population in practice. We instead present an analysis of data visualizations found in a real-world discourse of a significant global event---Twitter posts with visualizations related to the COVID-19 pandemic. Our work shows that, contrary to conventional wisdom, violations of visualization design guidelines are not the dominant way people mislead with charts. Specifically, they do not disproportionately lead to reasoning errors in posters' arguments. Through a series of examples, we present common reasoning errors and discuss how even faithfully plotted data visualizations can be used to support misinformation online.