This paper investigates the role of text in visualizations, specifically the impact of text position, semantic content, and biased wording. Two empirical studies were conducted based on two tasks (predicting data trends and appraising bias) using two visualization types (bar and line charts). While the addition of text had a minimal effect on how people perceive data trends, there was a significant impact on how biased they perceive the authors to be. This finding revealed a relationship between the degree of bias in textual information and the perception of the authors' bias. Exploratory analyses support an interaction between a person's prediction and the degree of bias they perceived. This paper also develops a crowdsourced method for creating chart annotations that range from neutral to highly biased. This research highlights the need for designers to mitigate potential polarization of readers' opinions based on how authors' ideas are expressed.

翻译：本文探讨了文本在可视化中的作用，特别是文本位置、语义内容和有偏措辞的影响。基于两项任务（预测数据趋势和评估偏见），通过两种可视化类型（柱状图和折线图）进行了两项实证研究。虽然添加文本对人们感知数据趋势的影响微乎其微，但它显著影响了人们对作者偏见的感知。这一发现揭示了文本信息中的偏见程度与对作者偏见的感知之间存在关联。探索性分析支持了个人预测与所感知到的偏见程度之间的交互作用。本文还开发了一种众包方法，用于生成从无偏到高度偏见的图表注释。这项研究强调，设计者需要根据作者观点的表达方式，缓解读者观点的潜在极化。