# NoisyHate: Mining Online Human-Written Perturbations for Realistic Robustness Benchmarking of Content Moderation Models

arXiv: https://arxiv.org/abs/2303.10430 · HTML: https://mlsanity.com/paper/2303.10430/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 50 references that are in the map.
- First posted on arXiv 18 Mar 2023.

## Builds on (most cited first, 16 of 16)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment](https://mlsanity.com/paper/1907.11932/) (2019)
- [BERT-ATTACK: Adversarial Attack Against BERT Using BERT](https://mlsanity.com/paper/2004.09984/) (2020)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [Adversarial Examples: Attacks and Defenses for Deep Learning](https://mlsanity.com/paper/1712.07107/) (2017)
- [Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers](https://mlsanity.com/paper/1801.04354/) (2018)
- [BAE: BERT-based Adversarial Examples for Text Classification](https://mlsanity.com/paper/2004.01970/) (2020)
- [All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text](https://mlsanity.com/paper/2107.00061/) (2021)
- [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://mlsanity.com/paper/1903.08983/) (2019)
- [SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)](https://mlsanity.com/paper/2006.07235/) (2020)
- [Interpretable Adversarial Perturbation in Input Embedding Space for Text](https://mlsanity.com/paper/1805.02917/) (2018)
- [NeuSpell: A Neural Spelling Correction Toolkit](https://mlsanity.com/paper/2010.11085/) (2020)
- [CRYPTEXT: Database and Interactive Toolkit of Human-Written Text Perturbations in the Wild](https://mlsanity.com/paper/2301.06494/) (2023)
- [Data-Driven Mitigation of Adversarial Text Perturbation](https://mlsanity.com/paper/2202.09483/) (2022)

## Built on by (most cited first, 1 of 1)

- [ContiGuard: A Framework for Continual Toxicity Detection Against Evolving Evasive Perturbations](https://mlsanity.com/paper/2603.14843/) (2026)

## Abstract (arXiv)

Online texts with toxic content are a clear threat to the users on social media in particular and society in general. Although many platforms have adopted various measures (e.g., machine learning-based hate-speech detection systems) to diminish their effect, toxic content writers have also attempted to evade such measures by using cleverly modified toxic words, so-called human-written text perturbations. Therefore, to help build automatic detection tools to recognize those perturbations, prior methods have developed sophisticated techniques to generate diverse adversarial samples. However, we note that these ``algorithms"-generated perturbations do not necessarily capture all the traits of ``human"-written perturbations. Therefore, in this paper, we introduce a novel, high-quality dataset of human-written perturbations, named as NoisyHate, that was created from real-life perturbations that are both written and verified by human-in-the-loop. We show that perturbations in NoisyHate have different characteristics than prior algorithm-generated toxic datasets show, and thus can be in particular useful to help develop better toxic speech detection solutions. We thoroughly validate NoisyHate against state-of-the-art language models, such as BERT and RoBERTa, and black box APIs, such as Perspective API, on two tasks, such as perturbation normalization and understanding.