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README.md
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---
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language:
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license: mit
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pretty_name: RedNote Covert Advertisement Detection Dataset
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tags:
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- covert advertisement detection
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- social-media
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- image-text
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- multimodal
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- RedNote
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- Xiaohongshu
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datasets:
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- Jingyi77/CHASM-Covert_Advertisement_on_RedNote
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dataset_info:
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- config_name: default
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configs:
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- config_name: default
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size_categories:
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- 1K<n<10K
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---
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# RedNote Covert Advertisement Detection Dataset
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This dataset contains posts from the RedNote platform for covert advertisement detection tasks.
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## Dataset Overview
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| Split
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| Train
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| Validation | 499
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| Test
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| **Total**
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## Dataset Structure
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The dataset is divided into three parts:
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- `train.parquet`: Training set
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- `validation.parquet`: Validation set
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- `test.parquet`: Test set
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## Field Descriptions
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Each parquet file contains the following fields:
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- `id`: Unique identifier for each post
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- `title`: Post title
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- `description`: Post description content
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- `label`: Label (0=non-advertisement, 1=advertisement)
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- `split`: Data split (train/validation/test)
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##
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train_df = pd.read_parquet("train.parquet")
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print(f"Number of training samples: {len(train_df)}")
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print(f"Label distribution: {train_df['label'].value_counts()}")
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```
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```python
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import
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import io
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from PIL import Image
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#
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#
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img = Image.open(io.BytesIO(img_data))
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img.show()
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```
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- **Multimodal Data**: Each post contains both text (title, description, comments) and images
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- **Real-world Data**: Collected from actual social media posts on the RedNote platform
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- **Multiple Images**: Each post may contain multiple images (average of 5.27 images per post)
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## Citation
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```
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@dataset{CHASM,
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author = {Jingyi Zheng, Tianyi Hu, Yule Liu, Zhen Sun, Zongmin Zhang, Wenhan Dong, Zifan Peng},
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title = {CHASM: Unveiling Covert Advertisements on Chinese Social Media},
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year = {2025},
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publisher = {Hugging Face},
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journal = {Hugging Face Hub},
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howpublished = {\url{https://huggingface.co/datasets/Jingyi77/CHASM-Covert_Advertisement_on_RedNote}}
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}
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```
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---
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language:
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- zh
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license: mit
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pretty_name: RedNote Covert Advertisement Detection Dataset
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tags:
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- covert advertisement detection
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- social-media
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- image-text
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- multimodal
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- RedNote
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- Xiaohongshu
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datasets:
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- Jingyi77/CHASM-Covert_Advertisement_on_RedNote
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dataset_info:
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- config_name: default
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features:
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- name: id
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dtype: string
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- name: title
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dtype: string
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- name: description
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dtype: string
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- name: date
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dtype: string
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- name: comments
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sequence:
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dtype: string
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- name: images
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sequence:
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dtype: binary
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- name: image_count
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dtype: int32
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- name: label
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dtype: int8
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- name: split
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dtype: string
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configs:
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- config_name: default
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data_files:
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- split: test
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path: test.parquet
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- split: train
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path: train.parquet
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- split: validation
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path: validation.parquet
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size_categories:
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- 1K<n<10K
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---
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<!-- @format -->
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# RedNote Covert Advertisement Detection Dataset
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This dataset contains posts from the RedNote platform for covert advertisement detection tasks.
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## Dataset Overview
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| Split | Posts | Ad Posts | Non-Ad Posts | Total Images |
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| ---------- | -------- | -------- | ------------ | ------------ |
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| Train | 3493 | 426 | 3067 | 18543 |
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| Validation | 499 | 57 | 442 | 2678 |
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| Test | 1000 | 130 | 870 | 5103 |
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| **Total** | **4992** | **613** | **4379** | **26324** |
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## Field Descriptions
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Each parquet file contains the following fields:
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- `id`: Unique identifier for each post
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- `title`: Post title
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- `description`: Post description content
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- `label`: Label (0=non-advertisement, 1=advertisement)
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- `split`: Data split (train/validation/test)
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## Dataset Features
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- **Multimodal Data**: Each post contains both text (title, description, comments) and images
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- **Real-world Data**: Collected from actual social media posts on the RedNote platform
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- **Multiple Images**: Each post may contain multiple images (average of 5.27 images per post)
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## Data Format
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The dataset is stored in WebDataset format, with each sample containing:
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1. One or more image files (.jpg format)
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2. A JSON metadata file with the following fields:
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- `id`: Sample ID
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- `title`: Title
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- `description`: Description
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- `date`: Date
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- `comments`: List of comments
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- `label`: Label (0: non-advertisement, 1: advertisement)
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## Loading the Dataset
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```python
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from datasets import load_dataset
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# Load training set
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train_dataset = load_dataset("Jingyi77/CHASM-Covert_Advertisement_on_RedNote", split="train")
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# Load validation set
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val_dataset = load_dataset("Jingyi77/CHASM-Covert_Advertisement_on_RedNote", split="validation")
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# Load test set
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test_dataset = load_dataset("Jingyi77/CHASM-Covert_Advertisement_on_RedNote", split="test")
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# Access a sample
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example = train_dataset[0]
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metadata = {
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"id": example["id"],
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"title": example["title"],
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"description": example["description"],
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"label": example["label"]
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}
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images = example["images"] # List of images
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```
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## Citation
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```
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@dataset{CHASM,
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author = {Jingyi Zheng, Tianyi Hu, Yule Liu, Zhen Sun, Zongmin Zhang, Wenhan Dong, Zifan Peng, Xinlei He},
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title = {CHASM: Unveiling Covert Advertisements on Chinese Social Media},
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year = {2025},
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publisher = {Hugging Face},
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journal = {Hugging Face Hub},
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howpublished = {\url{https://huggingface.co/datasets/Jingyi77/CHASM-Covert_Advertisement_on_RedNote}}
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}
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```
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