# CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection

arXiv: https://arxiv.org/abs/2204.06389 · HTML: https://mlsanity.com/paper/2204.06389/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 34 references that are in the map.
- First posted on arXiv 13 Apr 2022.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection](https://mlsanity.com/paper/2203.09509/) (2022)
- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [HateBERT: Retraining BERT for Abusive Language Detection in English](https://mlsanity.com/paper/2010.12472/) (2020)
- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [A Benchmark Dataset for Learning to Intervene in Online Hate Speech](https://mlsanity.com/paper/1909.04251/) (2019)
- [Toxicity Detection: Does Context Really Matter?](https://mlsanity.com/paper/2006.00998/) (2020)
- [Generating Counter Narratives against Online Hate Speech: Data and Strategies](https://mlsanity.com/paper/2004.04216/) (2020)
- [Detecting Hate Speech in Social Media](https://mlsanity.com/paper/1712.06427/) (2017)
- [Ruddit: Norms of Offensiveness for English Reddit Comments](https://mlsanity.com/paper/2106.05664/) (2021)
- [Abuse is Contextual, What about NLP? The Role of Context in Abusive Language Annotation and Detection](https://mlsanity.com/paper/2103.14916/) (2021)
- [You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLP](https://mlsanity.com/paper/1909.00412/) (2019)
- [Fight Fire with Fire: Fine-tuning Hate Detectors using Large Samples of Generated Hate Speech](https://mlsanity.com/paper/2109.00591/) (2021)
- [Predictive Embeddings for Hate Speech Detection on Twitter](https://mlsanity.com/paper/1809.10644/) (2018)

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

- [Generative AI for Hate Speech Detection: Evaluation and Findings](https://mlsanity.com/paper/2311.09993/) (2023)
- [Putting Context in Context: the Impact of Discussion Structure on Text Classification](https://mlsanity.com/paper/2402.02975/) (2024)
- [Decoding Demographic un-fairness from Indian Names](https://mlsanity.com/paper/2209.03089/) (2022)
- [Fast Few shot Self-attentive Semi-supervised Political Inclination Prediction](https://mlsanity.com/paper/2209.10292/) (2022)
- [Personality Detection and Analysis using Twitter Data](https://mlsanity.com/paper/2309.05497/) (2023)

## Abstract (arXiv)

The last decade has witnessed a surge in the interaction of people through social networking platforms. While there are several positive aspects of these social platforms, the proliferation has led them to become the breeding ground for cyber-bullying and hate speech. Recent advances in NLP have often been used to mitigate the spread of such hateful content. Since the task of hate speech detection is usually applicable in the context of social networks, we introduce CRUSH, a framework for hate speech detection using user-anchored self-supervision and contextual regularization. Our proposed approach secures ~ 1-12% improvement in test set metrics over best performing previous approaches on two types of tasks and multiple popular english social media datasets.