NLP-Based Review for Toxic Comment Detection Tailored to the Chinese Cyberspace

TL;DR

This paper reviews NLP techniques for detecting toxic comments in Chinese cyberspace, highlighting challenges due to language complexity and proposing a scalable framework for better classification and interpretability.

Contribution

It introduces a novel fine-grained, scalable framework for toxic comment detection tailored to Chinese language nuances, addressing dataset construction and model interpretability.

Findings

Analysis of Chinese toxic comment characteristics

Review of detection models from traditional to deep learning

Discussion of open challenges and future directions

Abstract

With the in-depth integration of mobile Internet and widespread adoption of social platforms, user-generated content in the Chinese cyberspace has witnessed explosive growth. Among this content, the proliferation of toxic comments poses severe challenges to individual mental health, community atmosphere and social trust. Owing to the strong context dependence, cultural specificity and rapid evolution of Chinese cyber language, toxic expressions are often conveyed through complex forms such as homophones and metaphors, imposing notable limitations on traditional detection methods. To address this issue, this review focuses on the core topic of natural language processing based toxic comment detection in the Chinese cyberspace, systematically collating and critically analyzing the research progress and key challenges in this field. This review first defines the connotation and characteristics of Chinese toxic comments, and analyzes the platform ecology and transmission mechanisms they rely on. It then comprehensively reviews the construction methods and limitations of existing public datasets, and proposes a novel fine-grained and scalable framework for toxic comment definition and classification, along with corresponding data annotation and quality assessment strategies. We systematically summarize the evolutionary path of detection models from traditional methods to deep learning, with special emphasis on the importance of interpretability in model design. Finally, we thoroughly discuss the open challenges faced by current research and provide forward-looking suggestions for future research directions.