Background: Visualizations are used to communicate data about important political, social, environmental, and health topics to a wide range of audiences; however, perceptions of graphs as objective conduits of factual data make them an easy means for spreading misinformation. Research questions: 1. Are people deceived by common deceptive tactics or exaggerated titles used in data visualizations about non-controversial topics? 2. Does a person's previous data visualization coursework mitigate the extent to which they are deceived by deceptive tactics used in data visualizations? 3. What parts of data visualizations (title, shape, data labels) do people use to answer questions about the information being presented in data visualizations? Literature review: Although scholarship from psychology, human-computer interaction, and computer science has examined how data visualizations are processed by readers, scholars have not adequately researched how susceptible people are to a range of deceptive tactics used in data visualizations, especially when paired with textual content. Methodology: Participants (n = 329) were randomly assigned to view one of four treatments for four different graph types (bar, line, pie, and bubble) and then asked to answer a question about each graph. Participants were asked to rank the ease with which they read each graph and comment on what they used to respond to the question about each graph. Results/Discussion: Results show that deceptive tactics caused participants to misinterpret information in the deceptive versus control visualizations across all graph types. Neither graph titles nor previous coursework impacted responses for any of the graphs. Qualitative responses illuminate people's perceptions of graph readability and what information they use to read different types of graphs. Conclusions: Recommendations are made to improve data visualization instruction, including critically examining software defaults and the ease with which people give agency over to software when preparing data visualizations. Avenues of future research are discussed.

中文翻译：

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人们如何受到日常图表和图形中的欺骗性策略的影响

背景：可视化用于向广泛的受众传达有关重要政治、社会、环境和健康主题的数据；然而，将图表视为事实数据的客观渠道，使它们成为传播错误信息的简单手段。研究问题： 1. 人们是否被常见的欺骗策略或关于非争议主题的数据可视化中使用的夸大标题所欺骗？2. 一个人之前的数据可视化课程是否减轻了他们被数据可视化中使用的欺骗策略欺骗的程度？3. 人们使用数据可视化的哪些部分（标题、形状、数据标签）来回答有关数据可视化中呈现的信息的问题？文献综述：虽然来自心理学、人机交互、虽然计算机科学已经研究了读者如何处理数据可视化，但学者们还没有充分研究人们对数据可视化中使用的一系列欺骗策略的敏感程度，尤其是在与文本内容配对时。方法：参与者（n = 329）被随机分配查看四种不同图形类型（条形、线形、饼形和气泡）的四种处理之一，然后要求回答有关每个图形的问题。参与者被要求对他们阅读每个图表的难易程度进行排名，并评论他们用来回答有关每个图表的问题的内容。结果/讨论：结果表明，欺骗性策略导致参与者在所有图形类型的欺骗性与控制可视化中误解信息。图表标题和之前的课程作业都不会影响任何图表的反应。定性响应阐明了人们对图形可读性的看法以及他们使用哪些信息来阅读不同类型的图形。结论：提出了改进数据可视化教学的建议，包括严格检查软件默认设置以及人们在准备数据可视化时将代理权交给软件的难易程度。讨论了未来研究的途径。包括批判性地检查软件默认设置以及人们在准备数据可视化时将代理权交给软件的难易程度。讨论了未来研究的途径。包括批判性地检查软件默认设置以及人们在准备数据可视化时将代理权交给软件的难易程度。讨论了未来研究的途径。