Do personal narratives make thematic maps more persuasive? Integrating concrete examples into maps of the social determinants of health

Thematic maps about social inequalities can engage audiences, add context to policy debates, and change attitudes toward the issues. The field of communication has long compared the relative persuasiveness of this kind of abstract data versus concrete examples about individuals. While studies have compared the effectiveness of presenting both types of information alongside each other, the line between them is sometimes blurred in data visualization, which can incorporate individuals’ stories in innovative ways. One context in which incorporating examples within thematic maps may help is when discussing the social determinants of health because the complex relationship between individual and community is central to how the determinants influence health, and communication on this can be challenging. In this study, we randomly presented the UK public (N = 389) with maps incorporating varying levels of “exemplification” for three different social determinants: public transport, air pollution, and youth service provision. We tested how this affected engagement, credibility, and perceptions about the issues. Between-group analysis found few significant differences and therefore limited persuasive power. However, within-subject analysis indicated that the maps with individual-centered stories may be more persuasive but only among those less confident in their ability to interpret data visualizations. Maps of social inequalities that incorporate stories about individuals may be more engaging and persuasive to audiences less confident with statistics.In data visualization experiments, researchers should consider analyzing both differences between treatment groups and differences within subjects in their responses to different stimuli. Maps of social inequalities that incorporate stories about individuals may be more engaging and persuasive to audiences less confident with statistics. In data visualization experiments, researchers should consider analyzing both differences between treatment groups and differences within subjects in their responses to different stimuli.