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---
language:
- zh
license: mit
pretty_name: RedNote Covert Advertisement Detection Dataset
tags:
- covert advertisement detection
- social-media
- image-text
- multimodal
- RedNote
- Xiaohongshu
datasets:
- Jingyi77/CHASM-Covert_Advertisement_on_RedNote
dataset_info:
- config_name: default
features:
- name: id
dtype: string
- name: title
dtype: string
- name: description
dtype: string
- name: date
dtype: string
- name: comments
sequence:
dtype: string
- name: images
sequence:
dtype: string
- name: image_count
dtype: int32
- name: label
dtype: int8
- name: split
dtype: string
configs:
- config_name: Example
data_files:
- split: Example
path: example.parquet
- split: Train_1
path: train_part_1.parquet
- split: Train_2
path: train_part_2.parquet
- split: Train_3
path: train_part_3.parquet
- split: Train_4
path: train_part_4.parquet
- split: Test
path: test.parquet
- split: Validation
path: validation.parquet
size_categories:
- 10<n<100
---
<!-- @format -->
# RedNote Covert Advertisement Detection Dataset
This dataset contains posts from the RedNote platform for covert advertisement detection tasks.
## Dataset Overview
| Split | Posts | Ad Posts | Non-Ad Posts | Total Images |
| ---------- | -------- | -------- | ------------ | ------------ |
| Train | 3493 | 426 | 3067 | 18543 |
| Validation | 499 | 57 | 442 | 2678 |
| Test | 1000 | 130 | 870 | 5103 |
| **Total** | **4992** | **613** | **4379** | **26324** |
> Note: The viewer shows a **small example subset** of the data (60 samples) for demonstration purposes. The complete dataset is available via WebDataset format in the repository.
## Field Descriptions
The example parquet file contains the following fields:
- `id`: Unique identifier for each post
- `title`: Post title
- `description`: Post description content
- `date`: Publication date (format: MM-DD)
- `comments`: List of comments
- `images`: List of base64-encoded images
- `image_count`: Number of images
- `label`: Label (0=non-advertisement, 1=advertisement)
- `split`: Data split (train/validation/test)
## Dataset Features
- **Multimodal Data**: Each post contains both text (title, description, comments) and images
- **Real-world Data**: Collected from actual social media posts on the RedNote platform
- **Multiple Images**: Each post may contain multiple images (average of 5.27 images per post)
## Data Format
The complete dataset is stored in WebDataset format, with each sample containing:
1. One or more image files (.jpg format)
2. A JSON metadata file with the following fields:
- `id`: Sample ID
- `title`: Title
- `description`: Description
- `date`: Date
- `comments`: List of comments
- `label`: Label (0: non-advertisement, 1: advertisement)
## Loading the Dataset
```python
from datasets import load_dataset
# Load example dataset
dataset = load_dataset("Jingyi77/CHASM-Covert_Advertisement_on_RedNote")
# Access a sample
example = dataset[0]
metadata = {
"id": example["id"],
"title": example["title"],
"description": example["description"],
"label": example["label"]
}
images = example["images"] # List of images
```
## Citation
If you use this dataset in your research, please cite:
```
@dataset{CHASM,
author = {Jingyi Zheng, Tianyi Hu, Yule Liu, Zhen Sun, Zongmin Zhang, Wenhan Dong, Zifan Peng, Xinlei He},
title = {CHASM: Unveiling Covert Advertisements on Chinese Social Media},
year = {2025},
publisher = {Hugging Face},
journal = {Hugging Face Hub},
howpublished = {\url{https://huggingface.co/datasets/Jingyi77/CHASM-Covert_Advertisement_on_RedNote}}
}
```