# A Group-Specific Approach to NLP for Hate Speech Detection

arXiv: https://arxiv.org/abs/2304.11223 · HTML: https://mlsanity.com/paper/2304.11223/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 50 references that are in the map.
- First posted on arXiv 21 Apr 2023.

## Builds on (most cited first, 22 of 22)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://mlsanity.com/paper/2009.11462/) (2020)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020)
- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models](https://mlsanity.com/paper/2010.00133/) (2020)
- [KILT: a Benchmark for Knowledge Intensive Language Tasks](https://mlsanity.com/paper/2009.02252/) (2020)
- [The Woman Worked as a Babysitter: On Biases in Language Generation](https://mlsanity.com/paper/1909.01326/) (2019)
- [Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing](https://mlsanity.com/paper/2001.00973/) (2020)
- [Racial Bias in Hate Speech and Abusive Language Detection Datasets](https://mlsanity.com/paper/1905.12516/) (2019)
- [Detoxifying Language Models Risks Marginalizing Minority Voices](https://mlsanity.com/paper/2104.06390/) (2021)
- [A Survey of Race, Racism, and Anti-Racism in NLP](https://mlsanity.com/paper/2106.11410/) (2021)
- [A Quantitative Approach to Understanding Online Antisemitism](https://mlsanity.com/paper/1809.01644/) (2018)
- [Reliability Testing for Natural Language Processing Systems](https://mlsanity.com/paper/2105.02590/) (2021)
- ["Subverting the Jewtocracy": Online Antisemitism Detection Using Multimodal Deep Learning](https://mlsanity.com/paper/2104.05947/) (2021)
- [Detecting the Hate Code on Social Media](https://mlsanity.com/paper/1703.05443/) (2017)
- [ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection](https://mlsanity.com/paper/2205.12495/) (2022)
- [Tracing Antisemitic Language Through Diachronic Embedding Projections: France 1789-1914](https://mlsanity.com/paper/1906.01440/) (2019)
- [Annotating Antisemitic Online Content. Towards an Applicable Definition of Antisemitism](https://mlsanity.com/paper/1910.01214/) (2019)
- [It's a Thin Line Between Love and Hate: Using the Echo in Modeling Dynamics of Racist Online Communities](https://mlsanity.com/paper/2012.01133/) (2020)
- [SalamNET at SemEval-2020 Task12: Deep Learning Approach for Arabic Offensive Language Detection](https://mlsanity.com/paper/2007.13974/) (2020)

## Abstract (arXiv)

Automatic hate speech detection is an important yet complex task, requiring knowledge of common sense, stereotypes of protected groups, and histories of discrimination, each of which may constantly evolve. In this paper, we propose a group-specific approach to NLP for online hate speech detection. The approach consists of creating and infusing historical and linguistic knowledge about a particular protected group into hate speech detection models, analyzing historical data about discrimination against a protected group to better predict spikes in hate speech against that group, and critically evaluating hate speech detection models through lenses of intersectionality and ethics. We demonstrate this approach through a case study on NLP for detection of antisemitic hate speech. The case study synthesizes the current English-language literature on NLP for antisemitism detection, introduces a novel knowledge graph of antisemitic history and language from the 20th century to the present, infuses information from the knowledge graph into a set of tweets over Logistic Regression and uncased DistilBERT baselines, and suggests that incorporating context from the knowledge graph can help models pick up subtle stereotypes.