mlsanityData to 7 Oct 2026

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

arXiv 2204.06389 ↗First posted 13 Apr 2022cs.CL cs.AI cs.CYcited by 5

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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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 17 of its 34 references that are in the map. First posted on arXiv 13 Apr 2022.

Builds on

17 of 34 references.

References · 17

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Supervised Contrastive Learningcited by 2,803
  4. 2020Don't Stop Pretraining: Adapt Language Models to Domains and Taskscited by 1,548
  5. 2022ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detectioncited by 419
  6. 2020HateXplain: A Benchmark Dataset for Explainable Hate Speech Detectioncited by 403
  7. 2020HateBERT: Retraining BERT for Abusive Language Detection in Englishcited by 206
  8. 2017Deep Learning for Hate Speech Detection in Tweetscited by 180
  9. 2019A Benchmark Dataset for Learning to Intervene in Online Hate Speechcited by 128
  10. 2020Toxicity Detection: Does Context Really Matter?cited by 93
  11. 2020Generating Counter Narratives against Online Hate Speech: Data and Strategiescited by 58
  12. 2017Detecting Hate Speech in Social Mediacited by 42
5 more
  1. 2021Ruddit: Norms of Offensiveness for English Reddit Commentscited by 26
  2. 2021Abuse is Contextual, What about NLP? The Role of Context in Abusive Language Annotation and Detectioncited by 19
  3. 2019You Shall Know a User by the Company It Keeps: Dynamic Representations for Social Media Users in NLPcited by 14
  4. 2021Fight Fire with Fire: Fine-tuning Hate Detectors using Large Samples of Generated Hate Speechcited by 13
  5. 2018Predictive Embeddings for Hate Speech Detection on Twittercited by 12

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Cited by year

Citing papers per year: 2022: 2; 2023: 2; 2024: 1.

5 papers.

  1. 2023Generative AI for Hate Speech Detection: Evaluation and Findingscited by 6
  2. 2024Putting Context in Context: the Impact of Discussion Structure on Text Classificationcited by 3
  3. 2022Decoding Demographic un-fairness from Indian Namescited by 2
  4. 2022Fast Few shot Self-attentive Semi-supervised Political Inclination Predictioncited by 2
  5. 2023Personality Detection and Analysis using Twitter Datacited by 1