IEEE VIS 2024 Content: The Role of Text in Visualizations: How Annotations Shape Perceptions of Bias and Influence Predictions

The Role of Text in Visualizations: How Annotations Shape Perceptions of Bias and Influence Predictions

Chase Stokes -

Cindy Xiong Bearfield -

Marti Hearst -

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DOI: 10.1109/TVCG.2023.3338451

Room: Bayshore V

2024-10-16T12:30:00ZGMT-0600Change your timezone on the schedule page
2024-10-16T12:30:00Z
Exemplar figure, described by caption below
Left: Study stimuli consisted of line and bar charts that were derived from prior work and designed to have ambiguous prediction outcomes. The experiments varied the text position and text content for these charts; examples of these stimuli from both studies are shown behind the baseline charts. Right: Two tasks were studied with crowdsourced participants: prediction of the outcome of the trend, and assessment of the bias of the visualization author using the assessment questions shown.
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Keywords

Visualization, text, annotation, perceived bias, judgment, prediction

Abstract

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 methodfor 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 howauthors' ideas are expressed.