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README.md3.97 kB
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dataset_info.json3.38 kB
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example.parquet28.4 MB
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test.parquet1.07 GB
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train_part_1.parquet1.01 GB
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README.md

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

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}}
}
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9.92 GB
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