How People Are Influenced by Deceptive Tactics in Everyday Charts and Graphs

TL;DR: Recommendations are made to improve data visualization instruction, including critically examining software defaults and the ease with which people give agency over to software when preparing data visualizations.

Abstract: Background: Visualizations are used to communicate data about important political, social, environmental, and health topics to a wide range of audiences; however, perceptions of graphs as objective conduits of factual data make them an easy means for spreading misinformation. Research questions: 1. Are people deceived by common deceptive tactics or exaggerated titles used in data visualizations about non-controversial topics? 2. Does a person's previous data visualization coursework mitigate the extent to which they are deceived by deceptive tactics used in data visualizations? 3. What parts of data visualizations (title, shape, data labels) do people use to answer questions about the information being presented in data visualizations? Literature review: Although scholarship from psychology, human-computer interaction, and computer science has examined how data visualizations are processed by readers, scholars have not adequately researched how susceptible people are to a range of deceptive tactics used in data visualizations, especially when paired with textual content. Methodology: Participants (n = 329) were randomly assigned to view one of four treatments for four different graph types (bar, line, pie, and bubble) and then asked to answer a question about each graph. Participants were asked to rank the ease with which they read each graph and comment on what they used to respond to the question about each graph. Results/Discussion: Results show that deceptive tactics caused participants to misinterpret information in the deceptive versus control visualizations across all graph types. Neither graph titles nor previous coursework impacted responses for any of the graphs. Qualitative responses illuminate people's perceptions of graph readability and what information they use to read different types of graphs. Conclusions: Recommendations are made to improve data visualization instruction, including critically examining software defaults and the ease with which people give agency over to software when preparing data visualizations. Avenues of future research are discussed.