language:
- zh
pretty_name: ToxiCN_MM
🎉2024.9 Our study, titled "Towards Comprehensive Detection of Chinese Harmful Meme", has been accepted to NeurIPS2024 Dataset & Benchmark Track! paper
Considering the potential risk of abuse, please fill out the following form to request the datasets: https://forms.gle/UN61ZNfTgMZKfMrv7. After we get your request, we will send the dataset to your email as soon as possible.
To prevent potential misuse and ensure the originality of our work, we are currently releasing only a subset of the dataset. We appreciate your understanding and look forward to sharing the complete dataset with the community at the appropriate time. Labels of memes are listed in label.csv
ToxiCN MM
We introduce ToxiCN MM, the first Chinese harmful meme dataset, which consists of 12,000 samples with fine-grained annotations for meme types.
We focus on both targeted harmful memes and those exhibiting potential toxicity without specific targets, including general offense, sexual innuendo, and dispirited culture.
Ethics Statement
Our study aims to facilitate the comprehensive detection of Chinese harmful memes and raise researchers' attention to non-English memes.
The social psychological community has recognized the harms of the harmful types we selected in the dataset.
We acknowledge the risk of malicious actors attempting to reverse-engineer memes. We strongly discourage and denounce such practices, emphasizing the necessity of human moderation to prevent them. All resources are intended solely for scientific research and are prohibited from commercial use. We believe the benefits of our proposed resources outweigh the associated risks.
We strictly follow the data use agreements of each public online social platform.
It is important to note that all data has been anonymized and does not include any personal information.
The opinions and findings contained in the samples of our presented dataset should not be interpreted as representing the views expressed or implied by the authors.
Accessibility
Our dataset is licensed under CC BY-NC 4.0.
Cite
If you want to use the resources, please cite the following paper. The camera-ready version of the paper will be released after the conference:
@article{lu2024towards,
title={Towards Comprehensive Detection of Chinese Harmful Memes},
author={Lu, Junyu and Xu, Bo and Zhang, Xiaokun and Wang, Hongbo and Zhu, Haohao and Zhang, Dongyu and Yang, Liang and Lin, Hongfei},
journal={arXiv preprint arXiv:2410.02378},
year={2024}
}