README.md

metadata

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: default
    data_files:
      - split: train
        path: train/*.tar
      - split: test
        path: test/*.tar
      - split: validation
        path: val/*.tar
size_categories:
  - 1K<n<10K
task_categories:
  - text-classification

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

Field Descriptions

Each 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 images paths
• 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 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 training set
train_dataset = load_dataset("Jingyi77/CHASM-Covert_Advertisement_on_RedNote", split="train")

# Load validation set
val_dataset = load_dataset("Jingyi77/CHASM-Covert_Advertisement_on_RedNote", split="validation")

# Load test set
test_dataset = load_dataset("Jingyi77/CHASM-Covert_Advertisement_on_RedNote", split="test")

# Access a sample
example = train_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}}
}