--- 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 # 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}} } ```