Summary of Towards Comprehensive Detection Of Chinese Harmful Memes, by Junyu Lu et al.
Towards Comprehensive Detection of Chinese Harmful Memes
by Junyu Lu, Bo Xu, Xiaokun Zhang, Hongbo Wang, Haohao Zhu, Dongyu Zhang, Liang Yang, Hongfei Lin
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a comprehensive approach to detecting Chinese harmful memes on the internet. The authors construct the first dataset of its kind, ToxiCN MM, comprising 12,000 annotated samples for various meme types. They also propose a baseline detector called Multimodal Knowledge Enhancement (MKE), which leverages contextual information from language models to better understand Chinese memes. Experimental results demonstrate that detecting Chinese harmful memes is challenging for existing models but show the effectiveness of MKE in this task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Detecting Chinese harmful memes on the internet has become a significant problem, but there’s been little research in this area due to the lack of reliable datasets and effective detectors. This paper tries to solve this issue by creating a new dataset called ToxiCN MM and a detector that uses language models to understand Chinese memes better. The results show that existing methods aren’t very good at detecting these harmful memes, but the new detector does a much better job. |
