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    Correcting partisan misperceptions with data visualizations can reduce support for political extremism. Visualizing the full range of opposing views, not just averages or intervals, most effectively corrects these distortions and reduces animosity.

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    Area of Science:

    • Political Science
    • Social Psychology
    • Data Visualization

    Background:

    • Political sectarianism is exacerbated by misperceptions of opposing parties' views.
    • Overestimating support for extreme policies among political opponents fuels partisan animosity.
    • Corrective interventions can reduce support for political extremism, but their design's impact is understudied.

    Purpose of the Study:

    • To investigate how different data visualizations of outparty views affect correction of partisan misperceptions.
    • To compare the impact of showing only the average outparty view versus visualizing the range or full distribution of views.
    • To understand how visualization design influences reductions in support for political violence and antidemocratic actions.

    Main Methods:

    • An experiment was conducted with 483 U.S. participants (Democrats and Republicans).
    • Participants predicted outparty support for political violence and undemocratic practices.
    • Interventions involved presenting outparty views using different data visualizations: Mean-Only, Mean+Interval, or Mean+Points.

    Main Results:

    • The strongest reduction in support for political extremism was observed in the Mean-Only and Mean+Points conditions.
    • Correction effects were weaker when visualizing a 75% interval of outparty views (Mean+Interval condition).
    • Participants shown the full distribution (Mean+Points) were most accurate in recalling outparty views.

    Conclusions:

    • Data visualizations can effectively correct partisan misperceptions and reduce extremism.
    • The way variability in outparty views is visualized significantly impacts the effectiveness of corrective information.
    • Visualizing the full distribution of outparty views appears most effective for both correction and accurate recall.