While we typically focus on data visualization as a tool for facilitating cognitive tasks (e.g., learning facts, making decisions), we know relatively little about their second-order impacts on our opinions, attitudes, and values. For example, could design or framing choices interact with viewers' social cognitive biases in ways that promote political polarization? When reporting on U.S. attitudes toward public policies, it is popular to highlight the gap between Democrats and Republicans (e.g., with blue vs red connected dot plots). But these charts may encourage social-normative conformity, influencing viewers' attitudes to match the divided opinions shown in the visualization. We conducted three experiments examining visualization framing in the context of social conformity and polarization. Crowdworkers viewed charts showing simulated polling results for public policy proposals. We varied framing (aggregating data as non-partisan "All US Adults," or partisan "Democrat" and "Republican") and the visualized groups' support levels. Participants then reported their own support for each policy. We found that participants' attitudes biased significantly toward the group attitudes shown in the stimuli and this can increase inter-party attitude divergence. These results demonstrate that data visualizations can induce social conformity and accelerate political polarization. Choosing to visualize partisan divisions can divide us further.

翻译：尽管我们通常将数据可视化视为促进认知任务（如学习事实、做出决策）的工具，但对其在观点、态度和价值观方面的二阶影响知之甚少。例如，设计或框架选择是否会与观众的社会认知偏差相互作用，从而加剧政治极化？在报道美国公众对公共政策的态度时，强调民主党与共和党之间的分歧（例如，使用蓝色与红色连接的点状图）是一种常见做法。但这些图表可能鼓励社会规范性趋同，影响观众的态度以匹配可视化中呈现的分裂意见。我们通过三项实验，在社会趋同与极化的背景下考察了可视化框架的影响。众包工作者观看了展示公共政策提案模拟民调结果的图表。我们改变了数据框架（将数据聚合为无党派性质的“全体美国成年人”，或党派性质的“民主党人”与“共和党人”）以及可视化群体的支持水平。随后，参与者报告了他们对每项政策的个人支持度。研究发现，参与者的态度显著偏向于刺激材料中呈现的群体态度，这可能加剧党派间态度的分化。这些结果表明，数据可视化能够诱发社会趋同并加速政治极化。选择可视化党派分歧的方式可能进一步加深我们的社会裂痕。