Visualizing COVID-19 with data: the effects of individual differences on perception of data in news

Abstract

Mass media and public health organizations' efforts play a significant role in disseminating information and reducing the morbidity and mortality of infectious disease outbreaks. The vast amount of data generated about the pandemic led to the enormous spread of various data visualizations and infographics. Visuals served as the main tools that helped experts and journalists explain the consequences of the pandemic, communicate the facts, and persuade people to follow safety measures. Current research investigates how various formats of news messages such as data visualization and textual content affect an individual's perception of the message, such as message acceptance (positive attitudes about the message, intentions to follow prevention measures, and self-efficacy measure for behavior change), message rejection measures (defensive avoidance, negative attitudes about the message, reactance, anger) as well as credibility and effectiveness of the message. Political partisanship, need for cognition, and graphicacy were used as moderators. Results have demonstrated that the format of the message does not affect acceptance or rejection of the message, while moderators were significant predictors for dependent variables. The computational textual analysis illustrates the differences in topics between partisan groups where Democrats expressed more hope, positive sentiment, and more trust in vaccination, government, media, and science than independents and Republicans who were more prone to conspiracy theory thinking.