# Chinese Offensive Language Detection:Current Status and Future Directions

arXiv: https://arxiv.org/abs/2403.18314 · HTML: https://mlsanity.com/paper/2403.18314/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 49 references that are in the map.
- First posted on arXiv 27 Mar 2024.

## Builds on (most cited first, 15 of 15)

- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes](https://mlsanity.com/paper/2005.04790/) (2020)
- [Racial Bias in Hate Speech and Abusive Language Detection Datasets](https://mlsanity.com/paper/1905.12516/) (2019)
- [Understanding Abuse: A Typology of Abusive Language Detection Subtasks](https://mlsanity.com/paper/1705.09899/) (2017)
- [COLD: A Benchmark for Chinese Offensive Language Detection](https://mlsanity.com/paper/2201.06025/) (2022)
- [Offensive Language and Hate Speech Detection for Danish](https://mlsanity.com/paper/1908.04531/) (2019)
- [Facilitating Fine-grained Detection of Chinese Toxic Language: Hierarchical Taxonomy, Resources, and Benchmarks](https://mlsanity.com/paper/2305.04446/) (2023)
- [SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection](https://mlsanity.com/paper/2108.03070/) (2021)
- [Detecting Offensive Language in Tweets Using Deep Learning](https://mlsanity.com/paper/1801.04433/) (2018)
- [Cross-Cultural Transfer Learning for Chinese Offensive Language Detection](https://mlsanity.com/paper/2303.17927/) (2023)
- ["So You Think You're Funny?": Rating the Humour Quotient in Standup Comedy](https://mlsanity.com/paper/2110.12765/) (2021)
- [Offensive Language and Hate Speech Detection with Deep Learning and Transfer Learning](https://mlsanity.com/paper/2108.03305/) (2021)
- [BEIKE NLP at SemEval-2022 Task 4: Prompt-Based Paragraph Classification for Patronizing and Condescending Language Detection](https://mlsanity.com/paper/2208.01312/) (2022)
- [Robust Chinese Word Segmentation with Contextualized Word Representations](https://mlsanity.com/paper/1901.05816/) (2019)
- [Offensive Language Detection: A Comparative Analysis](https://mlsanity.com/paper/2001.03131/) (2020)

## Built on by (most cited first, 5 of 5)

- [ChineseWebText 2.0: Large-Scale High-quality Chinese Web Text with Multi-dimensional and fine-grained information](https://mlsanity.com/paper/2411.19668/) (2024)
- [AlbanianLLMSafety: A Safety Evaluation Dataset for Large Language Models in Albanian](https://mlsanity.com/paper/2605.26954/) (2026)
- [KZ-SafetyPrompts: A Kazakh Safety Evaluation Prompt Dataset for Large Language Models](https://mlsanity.com/paper/2605.26947/) (2026)
- [Cross-Platform Chinese Offensive Comment Detection via Dual-Threshold Hard Example Mining](https://mlsanity.com/paper/2606.27629/) (2026)
- [An Explainable System for Offensive Content Detection in Nastaliq Urdu: Corpus Curation, Transformer Benchmarking, and Word-Level Attribution](https://mlsanity.com/paper/2610.15795/) (2026)

## Abstract (arXiv)

Despite the considerable efforts being made to monitor and regulate user-generated content on social media platforms, the pervasiveness of offensive language, such as hate speech or cyberbullying, in the digital space remains a significant challenge. Given the importance of maintaining a civilized and respectful online environment, there is an urgent and growing need for automatic systems capable of detecting offensive speech in real time. However, developing effective systems for processing languages such as Chinese presents a significant challenge, owing to the language's complex and nuanced nature, which makes it difficult to process automatically. This paper provides a comprehensive overview of offensive language detection in Chinese, examining current benchmarks and approaches and highlighting specific models and tools for addressing the unique challenges of detecting offensive language in this complex language. The primary objective of this survey is to explore the existing techniques and identify potential avenues for further research that can address the cultural and linguistic complexities of Chinese.