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README.md

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-license: cc-by-nc-4.0
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+---
+language:
+- en
+size_categories:
+- 1K<n<10K
+viewer: false
+license: cc-by-nc-sa-4.0
+---
+# 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.”
+
+The dataset 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, this dataset 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{lalai2026misleadingvlm,
+  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},
+  year={2026}
+}
+```