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README.md

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+---
+license: cc-by-nc-4.0
+task_categories:
+  - image-classification
+language:
+  - en
+tags:
+  - visualization
+  - misinformation
+  - misleading-visualizations
+  - COVID-19
+pretty_name: TrueVisLies – COVID-19 Twitter Visualization Dataset
+size_categories:
+  - 1K-10K
+---
+
+# TrueVisLies Twitter Dataset
+
+This dataset is used in the paper:
+> **True (VIS) Lies: Analyzing How Generative AI Recognizes Intentionality, Rhetoric, and Misleadingness in Visualization Lies**
+
+It is a subset of the dataset collected in:
+> Maxim Lisnic, Cole Polychronis, Alexander Lex, and Marina Kogan. 2023. Misleading Beyond Visual Tricks: How People Actually Lie with Charts. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23). Association for Computing Machinery, New York, NY, USA, Article 817, 1–21. https://doi.org/10.1145/3544548.3580910
+
+## Dataset Overview
+
+This dataset contains Twitter (now X) posts sharing data visualizations related to COVID-19, collected between January 2020 and August 2021. Each entry pairs an image of a chart or graph with metadata about the tweet and expert annotations indicating whether the visualization is misleading and, if so, what type of error it contains.
+
+The dataset is intended for research on misleading visualization detection.
+
+**Image rights:** Images are shared for research purposes only. All original rights belong to X (Twitter) and the respective content authors.
+
+Errors in visualizations have been identified by Lisnic et al. (2023) https://doi.org/10.1145/3544548.3580910
+
+---
+
+## Dataset Structure
+
+The dataset consists of the following files:
+
+- **`index.csv`** — The main annotation index. Each row corresponds to one image and contains the `image_id`, the associated `tweet_id`, a binary `is_misleading` label, and 14 binary columns indicating which specific error types (if any) are present.
+- **`metadata.json`** — A JSON dictionary keyed by `tweet_id` (equivalent to `image_id`). Each entry includes the tweet author, publication date, tweet text, tweet URL, original image URL, image dimensions, and a structured `errors` field that subdivides annotations into `visualization_design_violations` and `reasoning_errors`.
+- **`images/`** — A folder containing all images in PNG format. Each file is named `{image_id}.png`, where `image_id` matches the corresponding entry in `index.csv` and `metadata.json`.
+
+---
+
+## Statistics
+
+| Property | Value |
+|---|---|
+| Total samples | 2,336 |
+| Misleading | 1,168 (50%) |
+| Not misleading | 1,168 (50%) |
+| Image format | PNG |
+
+### Error Type Distribution
+
+The table below reports the number of misleading images annotated with each error type. Note that a single image can be annotated with more than one error.
+
+| Error Type | Count |
+|---|---|
+| Causal inference | 356 |
+| Cherry-picking | 300 |
+| Value as area/volume | 253 |
+| Setting an arbitrary threshold | 243 |
+| Dual axis | 224 |
+| Issues with data validity | 67 |
+| Failure to account for statistical nuance | 61 |
+| Truncated axis | 54 |
+| Inverted axis | 35 |
+| Unclear encoding | 19 |
+| Misrepresentation of scientific studies | 18 |
+| Inappropriate encoding | 11 |
+| Uneven binning | 3 |
+| Incorrect reading of chart | 4 |
+
+---
+
+## Fields Reference
+
+### `index.csv`
+
+| Column | Type | Description |
+|---|---|---|
+| `image_id` | int64 | Unique image identifier (matches filename in `images/` and key in `metadata.json`) |
+| `tweet_id` | int64 | Twitter post identifier (matches key in `metadata.json`) |
+| `is_misleading` | int (0/1) | Whether the visualization is annotated as misleading |
+| `error[*]` | int (0/1) | One column per error type; 1 if the error is present |
+
+#### Column Descriptions
+- `image_id` unique identifier for the image (matches filename in `images/` and key in `metadata.json`)
+- `tweet_id` id of the tweet associated with the image (same as `image_id`)
+- `is_misleading` int (0/1) | Whether the visualization is annotated as misleading
+- `error[Truncated axis]` boolean, indicating whether the tweet contains a misleading visualization with this error.
+- `error[Dual axis]` boolean, indicating whether the tweet contains a misleading visualization with this error.
+- `error[Value as area/volume]` boolean, indicating whether the tweet contains a misleading visualization with this error.
+- `error[Inverted axis]` boolean, indicating whether the tweet contains a misleading visualization with this error.
+- `error[Uneven binning]` boolean, indicating whether the tweet contains a misleading visualization with this error.
+- `error[Unclear encoding]` boolean, indicating whether the tweet contains a misleading visualization with this error.
+- `error[Inappropriate encoding]` boolean, indicating whether the tweet contains a misleading visualization with this error.
+- `error[Cherry-picking]` boolean, indicating whether the tweet contains a misleading visualization with this error.
+- `error[Setting an arbitrary threshold]` boolean, indicating whether the tweet contains a misleading visualization with this error.
+- `error[Causal inference]` boolean, indicating whether the tweet contains a misleading visualization with this error.
+- `error[Issues with data validity]` boolean, indicating whether the tweet contains a misleading visualization with this error.
+- `error[Failure to account for statistical nuance]` boolean, indicating whether the tweet contains a misleading visualization with this error.
+- `error[Misrepresentation of scientific studies]` boolean, indicating whether the tweet contains a misleading visualization with this error.
+- `error[Incorrect reading of chart]` boolean, indicating whether the tweet contains a misleading visualization with this error.
+
+
+### `metadata.json`
+
+The file is a JSON dictionary keyed by `tweet_id` (equivalent to `image_id`). Each entry contains the following fields:
+
+| Field | Type | Description |
+|---|---|---|
+| `tweet_id` | string | Twitter post identifier |
+| `is_misleading` | bool | Whether the visualization is misleading |
+| `author` | string | Display name of the tweet author |
+| `date` | string (ISO 8601) | Publication timestamp |
+| `text` | string | Full text of the tweet |
+| `tweet_url` | string | Permalink to the original tweet on X |
+| `image_url` | string | URL of the original image as hosted by Twitter |
+| `image_width` | int | Image width in pixels |
+| `image_height` | int | Image height in pixels |
+| `errors.visualization_design_violations` | list of strings | Design-level errors present in the visualization |
+| `errors.reasoning_errors` | list of strings | Reasoning-level errors present in the visualization |
+| `image_id` | string | Image identifier (matches `image_id` in `index.csv`) |
+
+---
+
+## Usage
+
+```python
+import pandas as pd
+import json
+from PIL import Image
+
+# Load annotations
+index = pd.read_csv("index.csv")
+
+# Load metadata
+with open("metadata.json") as f:
+    metadata = json.load(f)
+
+# Load an image
+image_id = index.iloc[0]["image_id"]
+img = Image.open(f"images/{image_id}.png")
+```
+
+---
+
+## License and Rights
+
+Images shared in this dataset are made available **for research purposes only**. All original rights to the images belong to **X (Twitter)** and the respective tweet authors.
+
+---
+
+## Citation
+
+If you use this dataset in your research, please cite the associated paper.