Projects

This paper investigates the impact of using anthropomorphized data graphics over standard charts on viewers’ empathy for, and prosocial behavior toward suffering populations, in the context of human rights narratives. We present a series of experiments conducted on Amazon Mechanical Turk, in which we compare various forms of anthropomorphized data graphics—ranging from a single human figure that ‘fills up’ to show proportional data, to separated groups of individual human beings—with a standard chart baseline. Each experiment uses two carefully crafted human rights data-driven stories to present the graphics. Contrary to our expectations, we consistently find that anthropomorphized data graphics and standard charts have very similar effects on empathy and prosocial behavior.

In a world of ‘Big Data’, data visualization allows the viewer to explore curated data; the creator to quickly convey complex information; and advocates to vividly display their view of a better world. Fields as disparate as journalism, environmental advocacy, and development assistance are taking advantage of these data-filled times. A similar movement can be described for the realm of human rights advocacy—although at a much smaller scale. Human rights advocates have been increasingly using data to better understand rights violations and to communicate their findings and messages to targeted audiences, from the general public to policymakers and judicial bodies. While the use of data and visualization among human rights advocates is becoming more common, innovations are being taken up unevenly, and advocates admit that choices about approaches and techniques are largely based on anecdotal evidence. This article introduces the results of preliminary research into some of these questions that are the product of collaboration between researchers from a school of engineering and a school of law. It provides an initial assessment of the field, presenting the results of a study examining the use of data visualization and other visual features by Amnesty International and Human Rights Watch through content coding and expert interviews. It then offers the findings of two crowdsourced user studies into pressing questions in the visualization field which hold promise for human rights advocates seeking to communicate their messages through data visualization, and concludes by suggesting further areas for research.

In this paper, we present an empirical analysis of deceptive visualizations. We start with an in-depth analysis of what deception means in the context of data visualization, and categorize deceptive visualizations based on the type of deception they lead to. We identify popular distortion techniques and the type of visualizations those distortions can be applied to, and formalize why deception occurs with those distortions. We create four deceptive visualizations using the selected distortion techniques, and run a crowdsourced user study to identify the deceptiveness of those visualizations. We then present the findings of our study and show how deceptive each of these visual distortion techniques are, and for what kind of questions the misinterpretation occurs. We also analyze individual differences among participants and present the effect of some of those variables on participants’ responses. This paper presents a first step in empirically studying deceptive visualizations, and will pave the way for more research in this direction.

Data visualization has been used extensively to inform users. However, little research has been done to examine the effects of data visualization in influencing users or in making a message more persuasive. In this study, we present experimental research to fill this gap and present an evidence-based analysis of persuasive visualization. We built on persuasion research from psychology and user interfaces literature in order to explore the persuasive effects of visualization. In this experimental study we define the circumstances under which data visualization can make a message more persuasive, propose hypotheses, and perform quantitative and qualitative analyses on studies conducted to test these hypotheses. We compare visual treatments with data presented through bar charts and line charts on the one hand, treatments with data presented through tables on the other, and then evaluate their persuasiveness. The findings represent a first step in exploring the effectiveness of persuasive visualization.