# Proactively Reducing the Hate Intensity of Online Posts via Hate Speech Normalization arXiv: https://arxiv.org/abs/2206.04007 ยท HTML: https://mlsanity.com/paper/2206.04007/ ## Key facts - Cited by 17 papers in the map (data through 14 Oct 2026). - Builds on 28 of its 51 references that are in the map. - First posted on arXiv 8 Jun 2022. ## Builds on (most cited first, 28 of 28) - [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019) - [The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes](https://mlsanity.com/paper/2005.04790/) (2020) - [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020) - [Toward Controlled Generation of Text](https://mlsanity.com/paper/1703.00955/) (2017) - [Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior](https://mlsanity.com/paper/1802.00393/) (2018) - [Style Transfer from Non-Parallel Text by Cross-Alignment](https://mlsanity.com/paper/1705.09655/) (2017) - [Latent Hatred: A Benchmark for Understanding Implicit Hate Speech](https://mlsanity.com/paper/2109.05322/) (2021) - [Racial Bias in Hate Speech and Abusive Language Detection Datasets](https://mlsanity.com/paper/1905.12516/) (2019) - [Hate Speech Dataset from a White Supremacy Forum](https://mlsanity.com/paper/1809.04444/) (2018) - [Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer](https://mlsanity.com/paper/1804.06437/) (2018) - [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017) - [Deep Learning for Text Style Transfer: A Survey](https://mlsanity.com/paper/2011.00416/) (2020) - [A Benchmark Dataset for Learning to Intervene in Online Hate Speech](https://mlsanity.com/paper/1909.04251/) (2019) - [CONAN -- COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech](https://mlsanity.com/paper/1910.03270/) (2019) - [Automatically Neutralizing Subjective Bias in Text](https://mlsanity.com/paper/1911.09709/) (2019) - [Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation](https://mlsanity.com/paper/1905.05621/) (2019) - [Politeness Transfer: A Tag and Generate Approach](https://mlsanity.com/paper/2004.14257/) (2020) - [Detecting Online Hate Speech Using Context Aware Models](https://mlsanity.com/paper/1710.07395/) (2017) - [Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach](https://mlsanity.com/paper/1805.05181/) (2018) - [Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer](https://mlsanity.com/paper/1805.07685/) (2018) - [Racism is a Virus: Anti-Asian Hate and Counterspeech in Social Media during the COVID-19 Crisis](https://mlsanity.com/paper/2005.12423/) (2020) - [LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer](https://mlsanity.com/paper/2105.08206/) (2021) - [A Unified Deep Learning Architecture for Abuse Detection](https://mlsanity.com/paper/1802.00385/) (2018) - [Can Cascades be Predicted?](https://mlsanity.com/paper/1403.4608/) (2014) - [Towards A Friendly Online Community: An Unsupervised Style Transfer Framework for Profanity Redaction](https://mlsanity.com/paper/2011.00403/) (2020) - [Countering Online Hate Speech: An NLP Perspective](https://mlsanity.com/paper/2109.02941/) (2021) - [Reconsidering Tweets: Intervening During Tweet Creation Decreases Offensive Content](https://mlsanity.com/paper/2112.00773/) (2021) - [Deep Exogenous and Endogenous Influence Combination for Social Chatter Intensity Prediction](https://mlsanity.com/paper/2006.07812/) (2020) ## Built on by (most cited first, 17 of 17) - [Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models](https://mlsanity.com/paper/2401.16727/) (2024) - [Counterspeeches up my sleeve! Intent Distribution Learning and Persistent Fusion for Intent-Conditioned Counterspeech Generation](https://mlsanity.com/paper/2305.13776/) (2023) - [Tox-BART: Leveraging Toxicity Attributes for Explanation Generation of Implicit Hate Speech](https://mlsanity.com/paper/2406.03953/) (2024) - [HateRephrase: Zero- and Few-Shot Reduction of Hate Intensity in Online Posts using Large Language Models](https://mlsanity.com/paper/2310.13985/) (2023) - [Revisiting Hate Speech Benchmarks: From Data Curation to System Deployment](https://mlsanity.com/paper/2306.01105/) (2023) - [Decoding Hate: Exploring Language Models' Reactions to Hate Speech](https://mlsanity.com/paper/2410.00775/) (2024) - [LLM-C3MOD: A Human-LLM Collaborative System for Cross-Cultural Hate Speech Moderation](https://mlsanity.com/paper/2503.07237/) (2025) - [Distilling Knowledge from Large Language Models: A Concept Bottleneck Model for Hate and Counter Speech Recognition](https://mlsanity.com/paper/2508.08274/) (2025) - [SAFE-MEME: Structured Reasoning Framework for Robust Hate Speech Detection in Memes](https://mlsanity.com/paper/2412.20541/) (2024) - [Probing Critical Learning Dynamics of PLMs for Hate Speech Detection](https://mlsanity.com/paper/2402.02144/) (2024) - [Overview of the HASOC Subtrack at FIRE 2023: Identification of Tokens Contributing to Explicit Hate in English by Span Detection](https://mlsanity.com/paper/2311.09834/) (2023) - [Counterspeech the ultimate shield! Multi-Conditioned Counterspeech Generation through Attributed Prefix Learning](https://mlsanity.com/paper/2505.11958/) (2025) - [Will I Get Hate Speech Predicting the Volume of Abusive Replies before Posting in Social Media](https://mlsanity.com/paper/2503.03005/) (2025) - [DeHate: A Stable Diffusion-based Multimodal Approach to Mitigate Hate Speech in Images](https://mlsanity.com/paper/2509.21787/) (2025) - [HCDIR: End-to-end Hate Context Detection, and Intensity Reduction model for online comments](https://mlsanity.com/paper/2312.13193/) (2023) - [Unveiling Social Media Comments with a Novel Named Entity Recognition System for Identity Groups](https://mlsanity.com/paper/2405.13011/) (2024) - [AEGIS: Awareness-Enhanced Guidance for Iterative Safeguard](https://mlsanity.com/paper/2607.17713/) (2026) ## Abstract (arXiv) Curbing online hate speech has become the need of the hour; however, a blanket ban on such activities is infeasible for several geopolitical and cultural reasons. To reduce the severity of the problem, in this paper, we introduce a novel task, hate speech normalization, that aims to weaken the intensity of hatred exhibited by an online post. The intention of hate speech normalization is not to support hate but instead to provide the users with a stepping stone towards non-hate while giving online platforms more time to monitor any improvement in the user's behavior. To this end, we manually curated a parallel corpus - hate texts and their normalized counterparts (a normalized text is less hateful and more benign). We introduce NACL, a simple yet efficient hate speech normalization model that operates in three stages - first, it measures the hate intensity of the original sample; second, it identifies the hate span(s) within it; and finally, it reduces hate intensity by paraphrasing the hate spans. We perform extensive experiments to measure the efficacy of NACL via three-way evaluation (intrinsic, extrinsic, and human-study). We observe that NACL outperforms six baselines - NACL yields a score of 0.1365 RMSE for the intensity prediction, 0.622 F1-score in the span identification, and 82.27 BLEU and 80.05 perplexity for the normalized text generation. We further show the generalizability of NACL across other platforms (Reddit, Facebook, Gab). An interactive prototype of NACL was put together for the user study. Further, the tool is being deployed in a real-world setting at Wipro AI as a part of its mission to tackle harmful content on online platforms.