File size: 3,973 Bytes
c3d641e b9cc7e4 c3d641e b9cc7e4 c3d641e b9cc7e4 7dfe470 b9cc7e4 7dfe470 c84ac62 b9cc7e4 c84ac62 b9cc7e4 0870ac1 a2f4704 585c6e2 b9cc7e4 c3d641e b8b4ac0 311bc5d c3d641e b8b4ac0 c3d641e b8b4ac0 c3d641e b8b4ac0 311bc5d c3d641e b9cc7e4 c3d641e b9cc7e4 311bc5d c3d641e b9cc7e4 c3d641e 311bc5d 67b5717 311bc5d 67b5717 311bc5d 67b5717 b9cc7e4 67b5717 311bc5d 18feb52 311bc5d 67b5717 311bc5d 88122aa b9cc7e4 c3d641e 311bc5d b9cc7e4 311bc5d c3d641e 05481cc 311bc5d 05481cc c3d641e 1c898c7 c3d641e b9cc7e4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 |
---
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}}
}
```
|