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AI Research
arXiv CS.AI·3d ago·5 min read
Breaking the Cloak! Unveiling Chinese Cloaked Toxicity with Homophone Graph and Toxic Lexicon
TL;DR
C$^2$TU is a novel method for unveiling cloaked toxic content in Chinese social media, achieving up to 71% improvement in F1 score over existing methods. It utilizes a homophone graph and toxic lexicon for effective detection and correction of disguised toxic words.
✦ Why It Matters
Implement C$^2$TU to enhance your content moderation systems for Chinese social media platforms today.
Key Takeaways
- Explore C$^2$TU for detecting cloaked toxicity in Chinese texts.
- Utilize homophone graphs to improve content moderation accuracy.
- Consider BERT and LLMs for advanced filtering of toxic content.
- Leverage the findings to enhance existing moderation tools.
How It Works
C$^2$TU identifies candidate toxic words by matching substrings against a homophone graph, which maps similar-sounding words in Chinese. It then filters these candidates using BERT and LLMs to distinguish between toxic and non-toxic words, correcting any cloaked terms to their original toxic forms.
The LLM variant specifically addresses the challenge of calculating word probabilities by utilizing the entire context of the text, leading to more accurate detection.