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Datasets:
UKPLab
/
misviz

Tasks:
Image Classification
Modalities:
Image
Text
Formats:
imagefolder
Sub-tasks:
multi-label-classification
Languages:
English
Size:
1K - 10K
ArXiv:
Tags:
data-visualization
misinformation
multimodal
chart-understanding
Libraries:
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metadata 
language:
  - en
license: cc-by-sa-4.0
pretty_name: Misviz
size_categories:
  - 1K<n<10K
task_categories:
  - image-classification
task_ids:
  - multi-label-classification
tags:
  - data-visualization
  - misinformation
  - multimodal
  - chart-understanding
annotations_creators:
  - expert-annotated

Dataset Card for Misviz

Dataset Description

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. A misleader refers to a specific misleading visualization practice, such as truncated axes, distorted proportions, or misleading annotations. The following misleaders are included: misrepresentation, 3D, truncated axis, inappropriate use of pie chart, inconsistent binning size, discretized continuous variable, inconsistent tick intervals, dual axis, inappropriate use of line chart, inappropriate item order, inverted axis, and inappropriate axis range.

The dataset contains:

  • The visualization image
  • One or more chart type labels
  • One or more misleader labels
  • Optional bounding boxes localizing misleading regions

Use cases

  • Multi-label classification: predicting which misleaders affect a visualization
  • Misleader localization: predicting bounding boxes around misleading regions

misviz_examples

Dataset Structure

Data Fields

Each entry contains the following fields:

  • image
    The visualization image

  • chart_type
    A list containing one or more chart types present in the visualization.

  • 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 boxes identifying the regions where a misleader is present.

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, HuggingFace's naming conventions 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 corpora were already annotated with misleaders in prior work.
  • Crowdworkers on Prolific annotated images from the subreddits

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 format differs slightly from the format used in the main repository. To reproduce the experiments from the paper, we recommend using the misviz.json file available in the GitHub repository.
  • The dataset contains 2,604 visualizations, which is a moderate scale.
  • The dataset does not cover all types of misleaders proposed in the existing taxonomies

Licensing Information

The dataset annotations are released under a CC-BY-SA 4.0 license. The dataset creators do not hold copyright for the visualization 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