---
title: "Is this chart lying to me? Automating the detection of misleading"
description: "Charts that break design conventions, for example through truncated axes or misleading 3D effects, can… 43 references, cited by 1 paper here."
authors: ["Jonathan Tonglet", "Jan Zimny", "Tinne Tuytelaars", "Iryna Gurevych"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.398/"
doi: "10.18653/v1/2026.acl-long.398"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/tonglet-2026-is-this-chart-lying-me/"
---
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# Is this chart lying to me? Automating the detection of misleading visualizations

**Authors:** [Jonathan Tonglet](https://md.research.elamur.ai/free/en/authors/jonathan-tonglet/), Jan Zimny, [Tinne Tuytelaars](https://md.research.elamur.ai/free/en/authors/tinne-tuytelaars/) and [Iryna Gurevych](https://md.research.elamur.ai/free/en/authors/iryna-gurevych/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.398/) · [PDF](https://aclanthology.org/2026.acl-long.398.pdf) · [DOI 10.18653/v1/2026.acl-long.398](https://doi.org/10.18653/v1/2026.acl-long.398) · [OpenAlex](https://openalex.org/W4415105208)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary
Charts that break design conventions, for example through truncated axes or misleading 3D effects, can distort data and spread misinformation online. Both people and multimodal LLMs are often fooled by them. Earlier detection work was hard to compare because its datasets were small or not openly available. The authors frame the task as multi-label classification and release two open resources. Misviz contains 2,604 real-world charts labeled with 12 misleader types. Misviz-synth contains 57,665 Matplotlib charts built from real data tables.

They compare three families of detectors:
- state-of-the-art MLLMs;
- a new rule-based linter that checks axis metadata;
- new classifiers that use the image alone or together with axis information.

The last two rely on a fine-tuned DePlot model to extract axis metadata. MLLMs do best on real-world charts, while the linter and image-axis classifiers win on synthetic data. However, axis extraction learned from synthetic charts does not transfer well to real ones, which lowers the real-world performance of these pipelines. The task remains far from solved, and these benchmarks give the field a shared basis for comparison.

## Key points
- **Misleader selection.** The 12 types were chosen from the 74-category taxonomy of Lo et al. (2022). The authors excluded rare, reasoning-based, merely confusing and domain-knowledge-dependent types. The selected types cover 62.3% of instances in that corpus.
- **Misviz sources and composition.** Charts come from the Lo et al. corpus, WTF Visualizations, r/dataisugly and r/dataisbeautiful. About 70% are misleading, with one to three misleaders each, and roughly 30% are clean. In size, Misviz exceeds the collection released by Lo and Qu (2025) by a factor of more than 15.
- **Misviz-synth contents.** Each chart comes with its data table, Python code and axis metadata, which makes it usable for training chart de-rendering models such as DePlot. It is limited to single-chart, single-misleader cases.
- **Localization experiment.** A small test asked Qwen2.5-VL-72B to draw bounding boxes around misleaders. It reached only 13.1% IoU on the Misviz validation set.
- **Licensing and access.** Code is released under Apache 2.0 and annotations under CC BY-SA 4.0 (github.com/UKPLab/acl2026-misviz). Images are not redistributed; instead, the dataset provides image URLs plus Wayback Machine archive links.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Jonathan Tonglet, Jan Zimny, Tinne Tuytelaars and Iryna Gurevych. 2026. Is this chart lying to me? Automating the detection of misleading visualizations. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{tonglet2026is,
  title = {{Is this chart lying to me? Automating the detection of misleading visualizations}},
  author = {Jonathan Tonglet, Jan Zimny, Tinne Tuytelaars and Iryna Gurevych},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.398},
  url = {https://doi.org/10.18653/v1/2026.acl-long.398}
}
```

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*Updated 31 October 2026*