  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.

Human-Computer Interaction