# STATE ToxiCN: A Benchmark for Span-level Target-Aware Toxicity Extraction in Chinese Hate Speech Detection

arXiv: https://arxiv.org/abs/2501.15451 · HTML: https://mlsanity.com/paper/2501.15451/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 26 Jan 2025.

## Builds on (most cited first, 12 of 12)

- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior](https://mlsanity.com/paper/1802.00393/) (2018)
- [HateBERT: Retraining BERT for Abusive Language Detection in English](https://mlsanity.com/paper/2010.12472/) (2020)
- [COLD: A Benchmark for Chinese Offensive Language Detection](https://mlsanity.com/paper/2201.06025/) (2022)
- [ShieldLM: Empowering LLMs as Aligned, Customizable and Explainable Safety Detectors](https://mlsanity.com/paper/2402.16444/) (2024)
- [Analyzing the Targets of Hate in Online Social Media](https://mlsanity.com/paper/1603.07709/) (2016)
- [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)
- [Towards Identifying Social Bias in Dialog Systems: Frame, Datasets, and Benchmarks](https://mlsanity.com/paper/2202.08011/) (2022)
- [ToxiCloakCN: Evaluating Robustness of Offensive Language Detection in Chinese with Cloaking Perturbations](https://mlsanity.com/paper/2406.12223/) (2024)

## Built on by (most cited first, 9 of 9)

- [Lost in Pronunciation: Detecting Chinese Offensive Language Disguised by Phonetic Cloaking Replacement](https://mlsanity.com/paper/2507.07640/) (2025)
- [System Report for CCL25-Eval Task 10: SRAG-MAV for Fine-Grained Chinese Hate Speech Recognition](https://mlsanity.com/paper/2507.18580/) (2025)
- [Exploring the Capability Boundaries of LLMs in Mastering of Chinese Chouxiang Language](https://mlsanity.com/paper/2604.15841/) (2026)
- [MER-Bench: A Comprehensive Benchmark for Multimodal Meme Reappraisal](https://mlsanity.com/paper/2603.15020/) (2026)
- [System Report for CCL25-Eval Task 10: Prompt-Driven Large Language Model Merge for Fine-Grained Chinese Hate Speech Detection](https://mlsanity.com/paper/2512.09563/) (2025)
- [Harder to Defend: Towards Chinese Toxicity Attacks via Implicit Enhancement and Obfuscation Rewriting](https://mlsanity.com/paper/2605.22258/) (2026)
- [AEGIS: Awareness-Enhanced Guidance for Iterative Safeguard](https://mlsanity.com/paper/2607.17713/) (2026)
- [SPAR-Hate: Auditor-Guided Multi-Perspective Role Reasoning for Bilingual Hate Speech Parsing](https://mlsanity.com/paper/2608.22018/) (2026)
- [Sledgehammer or Scalpel? A Fine-grained Adaptive Framework for Implicit Hate Speech](https://mlsanity.com/paper/2608.27462/) (2026)

## Abstract (arXiv)

The proliferation of hate speech has caused significant harm to society. The intensity and directionality of hate are closely tied to the target and argument it is associated with. However, research on hate speech detection in Chinese has lagged behind, and existing datasets lack span-level fine-grained annotations. Furthermore, the lack of research on Chinese hateful slang poses a significant challenge. In this paper, we provide a solution for fine-grained detection of Chinese hate speech. First, we construct a dataset containing Target-Argument-Hateful-Group quadruples (STATE ToxiCN), which is the first span-level Chinese hate speech dataset. Secondly, we evaluate the span-level hate speech detection performance of existing models using STATE ToxiCN. Finally, we conduct the first study on Chinese hateful slang and evaluate the ability of LLMs to detect such expressions. Our work contributes valuable resources and insights to advance span-level hate speech detection in Chinese.