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

arXiv: https://arxiv.org/abs/2401.04052 · HTML: https://mlsanity.com/paper/2401.04052/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jan 2024.

## Built on by (most cited first, 4 of 4)

- [C2: Scalable Auto-Feedback for LLM-based Chart Generation](https://mlsanity.com/paper/2410.18652/) (2024)
- [CHART-6: Human-Centered Evaluation of Data Visualization Understanding in Vision-Language Models](https://mlsanity.com/paper/2505.17202/) (2025)
- [AnnoBench: A Benchmark for Visualization Annotation Generation](https://mlsanity.com/paper/2607.25911/) (2026)
- [ChartAnno: Benchmarking Multimodal Large Language Models for Chart Annotation Generation](https://mlsanity.com/paper/2608.03464/) (2026)

## Abstract (arXiv)

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.