Correcting Misperceptions at a Glance: Using Data Visualizations to Reduce Political Sectarianism

TL;DR

This study examines how different data visualization methods influence the effectiveness of correcting misperceptions about political opponents, aiming to reduce political sectarianism and extremism.

Contribution

It demonstrates that visualizing the full distribution of outparty views enhances correction accuracy and understanding compared to simpler summaries.

Findings

Visualizations of full distributions improve correction accuracy.

Mean-only and full distribution visualizations are most effective.

Different visualization formats significantly influence perception correction.

Abstract

Political sectarianism is fueled in part by misperceptions of political opponents: People commonly overestimate the support for extreme policies among members of the other party. Research suggests that correcting partisan misperceptions by informing people about the actual views of outparty members may reduce one's own expressed support for political extremism, including partisan violence and anti-democratic actions. The present study investigated how correction effects depend on different representations of outparty views communicated through data visualizations. We conducted an experiment with U.S. based participants from Prolific (N=239 Democrats, N=244 Republicans). Participants made predictions about support for political violence and undemocratic practices among members of their political outparty. They were then presented with data from an earlier survey on the actual views of outparty members. Some participants viewed only the average response (Mean-Only condition), while other groups were shown visual representations of the range of views from 75% of the outparty (Mean+Interval condition) or the full distribution of responses (Mean+Points condition). Compared to a control group that was not informed about outparty views, we observed the strongest correction effects among participants in the Mean-only and Mean+Points condition, while correction effects were weaker in the Mean+Interval condition. In addition, participants who observed the full distribution of out-party views (Mean+Points condition) were most accurate at later recalling the degree of support among the outparty. Our findings suggest that data visualizations can be an important tool for correcting pervasive distortions in beliefs about other groups. However, the way in which variability in outparty views is visualized can significantly shape how people interpret and respond to corrective information.