README.md

metadata

language:
  - zh
license: mit
size_categories:
  - 1K<n<10K
pretty_name: CHASM - RedNote Covert Advertisement Detection Dataset
task_categories:
  - image-text-to-text
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

RedNote Covert Advertisement Detection Dataset (CHASM)

This dataset contains posts from the RedNote platform for covert advertisement detection tasks, introduced in the paper CHASM: Unveiling Covert Advertisements on Chinese Social Media.

Paper | Code

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:

@article{zheng2025chasm,
  title={CHASM: Unveiling Covert Advertisements on Chinese Social Media},
  author={Zheng, Jingyi and Hu, Tianyi and Liu, Yule and Sun, Zhen and Zhang, Zongmin and Dong, Wenhan and Peng, Zifan and He, Xinlei},
  journal={arXiv preprint arXiv:2604.20511},
  year={2025}
}