Dataset Card for Misviz

Dataset Summary

Misviz is a dataset of 2,604 real-world data visualizations collected from the web and manually annotated for misleading design practices. The dataset is introduced in the arXiv preprint Is this chart lying to me? Automating the detection of misleading visualizations.

Each visualization may contain up to three misleading design violations from a taxonomy of 12 misleaders. The dataset is designed to evaluate multimodal models for detecting misleading visualizations.

The dataset contains:

• The visualization image
• One or more chart types
• One or more misleaders
• Optional misleader bounding box annotations
• A predefined split field

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Use cases

• Multi-label classification: detecting whether a visualization is affected by 0, 1, 2, or 3 misleaders
• Misleader localization: drawing bounding boxes around the misleading areas of a visualization

Dataset Structure

Data Fields

Each entry contains the following fields:

• image
  The visualization image

• chart_type
  A list containing one or more chart type labels

• misleader
  A list of misleading design violations affecting the visualization. Each visualization may contain up to three misleaders from a taxonomy of 12.

• bbox
  Optional bounding box annotations for localizing misleaders.

Data Splits

The dataset contains predefined split labels:

• train (=dev)
• validation
• test

Note that the train set is not a real train set, but rather a small dev set that can be used for few-shot demonstrations retrieval. In the preprint, it is referred to as the dev set. However, the naming conventions of HuggingFace require us to call it the train set here.

To load the dataset, you will need to request access and then run the following script.

from datasets import load_dataset

ds = load_dataset("UKPLab/misviz", token="your_huggingface_token")

Dataset Creation

Curation Rationale

Misleading visualizations can distort public understanding of data and contribute to misinformation. While prior work has considered automating the detection of misleading charts, their datasets were either small or not publicly accessible. Misviz was created to provide the first large real-world benchmark for automated detection of misleading visualizations.

Data collection

The visualizations were collected from four sources.

• The corpus created by Lo et al. (2022) to construct their taxonomy of misleading visualizations
• The corpus created by Lan et Liu (2024) to construct their taxonomy of misleading visualizations
• The subreddit r/dataisugly, an online community to share and discuss examples of misleading visualizations
• The subreddit r/dataisbeautiful, an online community to share and discuss examples of non-misleading visualizations

Data annotation

• The first two corpus were already annotated with misleaders in prior work.
• Imaegs from the subreddits were annotated by crowdworkers on Prolific

Considerations for Using the Data

Social Impact of Dataset

This dataset supports research on detecting misleading visual content, which can help improve chart literacy, counter visual misinformation, and improve trust in data visualizations.

Known Limitations

• The HuggingFace dataset is not directly compatible with the main github repo. To reproduce our experiments, we rather recommend to use the json file misviz.json available in the github repo.
• The dataset contains 2,604 visualizations, which is a moderate scale.
• The dataset does not cover all types of misleaders from the existing taxonomies

Licensing Information

The dataset annotations are released under a CC-BY-SA 4.0 license. We do not own copyright over the images. The dataset should be used only for academic research.

Citation Information

If you find this dataset useful, please cite our paper as follows:

@article{tonglet2025misviz,
  title={Is this chart lying to me? Automating the detection of misleading visualizations},
  author={Tonglet, Jonathan and Zimny, Jan and Tuytelaars, Tinne and Gurevych, Iryna},
  journal={arXiv preprint arXiv:2508.21675},
  year={2025},
  url={https://arxiv.org/abs/2508.21675},
  doi={10.48550/arXiv.2508.21675}
}

Dataset Card Authors

Jonathan Tonglet

Dataset Card Contact

jonathan.tonglet@tu-darmstadt.de