Martina A. Rau’s research while affiliated with ETH Zurich and other places

Misinformation can have severe negative effects on people’s decisions, behaviors, and on society at large. This creates a need to develop and evaluate educational interventions that prepare people to recognize and respond to misinformation. We systematically review 107 articles describing educational interventions across various lines of research. In characterizing existing educational interventions, this review combines a theory-driven approach with a data-driven approach. The theory-driven approach uncovered that educational interventions differ in terms of how they define misinformation and regarding which misinformation characteristics they target. The data-driven approach uncovered that educational interventions have been addressed by research on the misinformation effect, lie detection, information literacy, and fraud trainings, with each line of research yielding different types of interventions. Furthermore, this article reviews evidence about the interventions’ effectiveness. Besides identifying several promising types of interventions, comparisons across different lines of research yield open questions that future research should address to identify ways to increase people's resilience towards misinformation.

Misleading data visualizations have become a significant issue in our information-rich world due to their negative impact on informed decision-making. Consequently, it is crucial to understand the factors that make viewers vulnerable to misleading data visualizations and to explore effective instructional supports that can help viewers combat the negative effects of such visualizations. Drawing upon the framework of graph comprehension, this article examines how poorly designed data visualizations can deceive viewers. A systematic review identified 26 pertinent articles that met our inclusion criteria. We identified two primary factors leading to viewers’ misinterpretations of misleading data visualizations: the graphical and contextual elements within the data visualizations themselves. Further, we identified two types of interventions aimed at reducing the negative impact of misleading data visualizations. One type of intervention focuses on providing external aids for viewers to recognize the misleading graphical and contextual elements within the data visualization. In contrast, another type of intervention aims at enhancing viewers’ ability to engage with data visualizations through additional interactions for reflection. Based on these findings, we identify areas that remain under-investigated, specifically those aiming at teaching viewers to interact with data visualizations. We conclude by proposing directions for future research to investigate interventions that strengthen viewers’ ability to go beyond their first (potentially false) impression with data visualizations through additional interactions with the data visualization.