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

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

Dataset Structure

The dataset is divided into three parts:

train.parquet: Training set
validation.parquet: Validation set
test.parquet: Test set

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 base64-encoded images
image_count: Number of images
label: Label (0=non-advertisement, 1=advertisement)
split: Data split (train/validation/test)

Usage

import pandas as pd

# Load the training set
train_df = pd.read_parquet("train.parquet")

# View dataset statistics
print(f"Number of training samples: {len(train_df)}")
print(f"Label distribution: {train_df['label'].value_counts()}")

Image Processing

Images are stored as base64 encoded strings. You can decode them using:

import base64
import io
from PIL import Image

# Get the first image from the first sample
base64_str = train_df.iloc[0]['images'][0]

# Decode base64 and display the image
img_data = base64.b64decode(base64_str)
img = Image.open(io.BytesIO(img_data))
img.show()

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)

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},
  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}}
}