---
language:
- en
size_categories:
- 1K<n<10K
viewer: false
license: cc-by-nc-sa-4.0
paper: https://arxiv.org/abs/2603.22368
repository: https://github.com/Harsh-Lalai/Evaluating-Vision-Language-Models-on-Misleading-Data-Visualizations
point_of_contact: lalaiharsh26@gmail.com
---

## Dataset Description

- **Repository:** https://github.com/Harsh-Lalai/Evaluating-Vision-Language-Models-on-Misleading-Data-Visualizations
- **Paper:** https://arxiv.org/abs/2603.22368
- **Point of Contact:** lalaiharsh26@gmail.com

# Evaluating Vision-Language Models on Misleading Data Visualizations (Dataset)

## Overview

This dataset accompanies the paper:

“[When Visuals Aren’t the Problem: Evaluating Vision-Language Models on Misleading Data Visualizations.](https://arxiv.org/abs/2603.22368)”

MisVisBench is designed to evaluate whether Vision-Language Models (VLMs) can detect misleading information in **data visualization-caption pairs**, and whether they can correctly attribute the source of misleadingness to appropriate error types: Caption-level reasoning errors and Visualization design errors.

Unlike prior benchmarks that primarily focus on chart understanding or visual distortions, MisVisBench enables **fine-grained analysis of misleadingness arising from both textual reasoning and visualization design choices**.

---

# Dataset Structure

![2x2 misleadingness grid](mislead_grid_2by2.png)

The dataset follows the **2 × 2 misleadingness decomposition** shown above.

2 × 2 mapping:
- **△** → caption-level reasoning errors, visualization is not misleading
- **○** → visualization design errors, caption is not misleading
- **■** → both caption and visualization are misleading
- **∅** → neither caption nor visualization is misleading (control)

The exact top-level keys in `data.json` are:
- `Misleading_Caption_Non_Misleading_Vis`
- `Non_Misleading_Caption_Misleading_Vis`
- `Misleading_Caption_Misleading_Vis`
- `Non_Misleading_Caption_Non_Misleading_Vis`

---

# Dataset Statistics

| Subset | Count |
|---|---:|
| **△** | 793 |
| **○** | 1110 |
| **■** | 501 |
| **∅** | 611 |
| **Total** | 3015 |

---

# Data Sources

| Subset | Source |
|---|---|
| **△** | X/Twitter |
| **○** | X/Twitter and subreddit DataIsUgly |
| **■** | X |
| **∅** | subreddit DataIsBeautiful |

Notes:
- For all samples sourced from **X**, we use the sample IDs from Lisnic et al. [1].
- In **○**, the first **601** samples are from **X** and the remaining samples are from **Reddit**.

---

# Dataset File

The dataset is provided as a **single JSON file**:

```
data.json
```

Structure:

```json
{
  "data_type_name": {
    "sample_id": {
      "reasoning_error_names": [...],
      "visualization_error_names": [...],
      "text": "... (only present for Misleading_Caption_Misleading_Vis samples)"
    }
  }
}
```

Example:

```json
{
  "Misleading_Caption_Non_Misleading_Vis": {
    "example_id1": {
      "reasoning_error_names": ["Cherry-picking", "Causal inference"],
      "visualization_error_names": null
    }
  },
  "Misleading_Caption_Misleading_Vis": {
    "example_id2": {
      "reasoning_error_names": ["Cherry-picking"],
      "visualization_error_names": ["Dual axis"],
      "text": "Example caption written by the authors that introduces reasoning errors."
    }
  }
}
```

---

# Dataset Fields

| Field | Description |
|---|---|
| **sample_id** | Identifier corresponding to the original post (tweet or Reddit post) |
| **reasoning_error_names** | List of caption-level reasoning errors present in the example |
| **visualization_error_names** | List of visualization design errors present in the chart |
| **text** | Caption text (**only provided for ■ samples**) |

### Important Note on the `text` Field

The **`text` field is only provided for ■ samples**.
For these samples:
- The captions were **written by the authors**
- The goal is to introduce specific **reasoning errors**
- The visualization is reused while the caption introduces the misleading reasoning
For the other three subsets (**△**, **○**, and **∅**), the dataset **does not include the caption text**, and therefore the `text` field is **not present** in those entries.

---

# Usage

The dataset can be loaded using the Hugging Face `datasets` library.

```python
from huggingface_hub import hf_hub_download
import json
# Download the raw JSON file from the dataset repo
json_path = hf_hub_download(
    repo_id="MaybeMessi/MisVisBench",
    repo_type="dataset",
    filename="data.json"
)
# Load the JSON
with open(json_path, "r", encoding="utf-8") as f:
    data = json.load(f)
# Iterate through the dataset
for category_name, samples in data.items():
    for sample_id, sample in samples.items():
        reasoning_errors = sample["reasoning_error_names"]
        visualization_errors = sample["visualization_error_names"]
        print("Category:", category_name)
        print("Sample ID:", sample_id)
        print("Reasoning Errors:", reasoning_errors)
        print("Visualization Errors:", visualization_errors)
        print()
```

# Error Taxonomy

## Caption-Level Reasoning Errors

- Cherry-picking  
- Causal inference  
- Setting an arbitrary threshold  
- Failure to account for statistical nuance  
- Incorrect reading of chart  
- Issues with data validity  
- Misrepresentation of scientific studies  

## Visualization Design Errors

- Truncated axis  
- Dual axis  
- Value encoded as area or volume  
- Inverted axis  
- Uneven binning  
- Unclear encoding  
- Inappropriate encoding  

## Examples: Caption-Level Reasoning Errors

<table style="width: 100%; table-layout: fixed; border-collapse: collapse;">
  <colgroup>
    <col style="width: 60%;" />
    <col style="width: 25%;" />
    <col style="width: 15%;" />
  </colgroup>
  <thead>
    <tr>
      <th width="60%" style="text-align: center;">Visualization</th>
      <th width="25%" style="text-align: left;">Caption</th>
      <th width="15%" style="text-align: center;">Reasoning Error</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style="text-align: center;"><img src="Examples/Cherry-picking.jpg" style="width: 360px; height: auto;"/></td>
      <td style="overflow-wrap: anywhere; text-align: left;">Reminder: Just because we've hit a peak does not mean we've hit THE peak.</td>
      <td style="overflow-wrap: anywhere; text-align: center;"><strong>Cherry-picking</strong></td>
    </tr>
    <tr>
      <td style="text-align: center;"><img src="Examples/Causal Inference.jpg" style="width: 360px; height: auto;"/></td>
      <td style="overflow-wrap: anywhere; text-align: left;">The positive impact of the UK's vaccination efforts in one graph</td>
      <td style="overflow-wrap: anywhere; text-align: center;"><strong>Causal inference</strong></td>
    </tr>
    <tr>
      <td style="text-align: center;"><img src="Examples/Setting Arb Threshold.jpg" style="width: 360px; height: auto;"/></td>
      <td style="overflow-wrap: anywhere; text-align: left;">This in a country of 56 million. Lift lockdown now, the virus is just gone.</td>
      <td style="overflow-wrap: anywhere; text-align: center;"><strong>Setting an arbitrary threshold</strong></td>
    </tr>
    <tr>
      <td style="text-align: center;"><img src="Examples/Stat Nuance.jpg" style="width: 360px; height: auto;"/></td>
      <td style="overflow-wrap: anywhere; text-align: left;">The numbers absolutely speak for themselves. Get vaccinated!</td>
      <td style="overflow-wrap: anywhere; text-align: center;"><strong>Failure to account for statistical nuance</strong></td>
    </tr>
    <tr>
      <td style="text-align: center;"><img src="Examples/Incorr Chart Reading.jpg" style="width: 360px; height: auto;"/></td>
      <td style="overflow-wrap: anywhere; text-align: left;">The flu is 10 times less deadly - particularly for elderly - than Covid!</td>
      <td style="overflow-wrap: anywhere; text-align: center;"><strong>Incorrect reading of chart</strong></td>
    </tr>
    <tr>
      <td style="text-align: center;"><img src="Examples/Data Val.jpg" style="width: 360px; height: auto;"/></td>
      <td style="overflow-wrap: anywhere; text-align: left;">This is a test of our humanity</td>
      <td style="overflow-wrap: anywhere; text-align: center;"><strong>Issues with data validity</strong></td>
    </tr>
    <tr>
      <td style="text-align: center;"><img src="Examples/Misrep Scientific Studies.jpg" style="width: 360px; height: auto;"/></td>
      <td style="overflow-wrap: anywhere; text-align: left;">SARS-Co∅2 positivity rates associated with circulating 25-hydroxyvitamin D levels (https://tinyurl.com/5n9xm536)</td>
      <td style="overflow-wrap: anywhere; text-align: center;"><strong>Misrepresentation of scientific studies</strong></td>
    </tr>
  </tbody>
</table>

## Examples: Visualization Design Errors

<table style="width: 100%; table-layout: fixed; border-collapse: collapse;">
  <colgroup>
    <col style="width: 60%;" />
    <col style="width: 25%;" />
    <col style="width: 15%;" />
  </colgroup>
  <thead>
    <tr>
      <th width="60%" style="text-align: center;">Visualization</th>
      <th width="25%" style="text-align: left;">Caption</th>
      <th width="15%" style="text-align: center;">Visualization Error</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td style="text-align: center;"><img src="Examples/Truncated Axis.png" style="width: 360px; height: auto;"/></td>
      <td style="overflow-wrap: anywhere; text-align: left;">Respiratory deaths at 10 year low!</td>
      <td style="overflow-wrap: anywhere; text-align: center;"><strong>Truncated axis</strong></td>
    </tr>
    <tr>
      <td style="text-align: center;"><img src="Examples/Dual Axis.jpg" style="width: 360px; height: auto;"/></td>
      <td style="overflow-wrap: anywhere; text-align: left;">May 17 Update: US COVID-19 Test Results: Test-and-Trace Success for Smallpox</td>
      <td style="overflow-wrap: anywhere; text-align: center;"><strong>Dual axis</strong></td>
    </tr>
    <tr>
      <td style="text-align: center;"><img src="Examples/Area Volume.jpg" style="width: 360px; height: auto;"/></td>
      <td style="overflow-wrap: anywhere; text-align: left;">Corona Virus Interactive Map.</td>
      <td style="overflow-wrap: anywhere; text-align: center;"><strong>Value encoded as area or volume</strong></td>
    </tr>
    <tr>
      <td style="text-align: center;"><img src="Examples/Inv Axis.jpg" style="width: 360px; height: auto;"/></td>
      <td style="overflow-wrap: anywhere; text-align: left;">Propaganda: RECORD NUMBER OF COVID POSITIVE CASES. Reality:</td>
      <td style="overflow-wrap: anywhere; text-align: center;"><strong>Inverted axis</strong></td>
    </tr>
    <tr>
      <td style="text-align: center;"><img src="Examples/Uneven Binning.jpeg" style="width: 360px; height: auto;"/></td>
      <td style="overflow-wrap: anywhere; text-align: left;">Interesting colour coding from the BBC</td>
      <td style="overflow-wrap: anywhere; text-align: center;"><strong>Uneven binning</strong></td>
    </tr>
    <tr>
      <td style="text-align: center;"><img src="Examples/Unclear Encoding.jpg" style="width: 360px; height: auto;"/></td>
      <td style="overflow-wrap: anywhere; text-align: left;">The Navajo Nation crushed the Covid curve. Success is possible.</td>
      <td style="overflow-wrap: anywhere; text-align: center;"><strong>Unclear encoding</strong></td>
    </tr>
    <tr>
      <td style="text-align: center;"><img src="Examples/Inappropriate Encoding.jpg" style="width: 360px; height: auto;"/></td>
      <td style="overflow-wrap: anywhere; text-align: left;">The worst pandemic of the most contagious disease we have seen for 100 years.</td>
      <td style="overflow-wrap: anywhere; text-align: center;"><strong>Inappropriate encoding</strong></td>
    </tr>
  </tbody>
</table>

---

# Dataset Purpose

This dataset enables evaluation of whether models can:

1. Detect misleading chart-caption pairs
2. Determine whether misleadingness arises from the **caption, visualization, or both**
3. Attribute misleadingness to **specific error categories**

This allows researchers to analyze how well VLMs handle **reasoning-based misinformation versus visualization design distortions**.

---

# References

[1] Lisnic, Maxim, Cole Polychronis, Alexander Lex, and Marina Kogan. "Misleading beyond visual tricks: How people actually lie with charts." In *Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems*, pp. 1-21. 2023.

---

# License

The dataset is released under the **CC-BY-NC-SA 4.0**.

---

# Contact

For any issues related to the dataset, feel free to reach out to lalaiharsh26@gmail.com

---

# Citation

```
@article{lalai2026visuals,
  title={When Visuals Aren't the Problem: Evaluating Vision-Language Models on Misleading Data Visualizations},
  author={Lalai, Harsh Nishant and Shah, Raj Sanjay and Pfister, Hanspeter and Varma, Sashank and Guo, Grace},
  journal={arXiv preprint arXiv:2603.22368},
  year={2026}
}
```