# Detecting White Supremacist Hate Speech using Domain Specific Word Embedding with Deep Learning and BERT

arXiv: https://arxiv.org/abs/2010.00357 · HTML: https://mlsanity.com/paper/2010.00357/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2020.

## Built on by (most cited first, 19 of 19)

- [Towards generalisable hate speech detection: a review on obstacles and solutions](https://mlsanity.com/paper/2102.08886/) (2021)
- [A systematic review of Hate Speech automatic detection using Natural Language Processing](https://mlsanity.com/paper/2106.00742/) (2021)
- [HBert + BiasCorp -- Fighting Racism on the Web](https://mlsanity.com/paper/2104.02242/) (2021)
- [Cross-lingual hate speech detection based on multilingual domain-specific word embeddings](https://mlsanity.com/paper/2104.14728/) (2021)
- [Manifestations of Xenophobia in AI Systems](https://mlsanity.com/paper/2212.07877/) (2022)
- [RLAS-BIABC: A Reinforcement Learning-Based Answer Selection Using the BERT Model Boosted by an Improved ABC Algorithm](https://mlsanity.com/paper/2301.02807/) (2023)
- [Identity Construction in a Misogynist Incels Forum](https://mlsanity.com/paper/2306.15745/) (2023)
- [Beyond Dataset Creation: Critical View of Annotation Variation and Bias Probing of a Dataset for Online Radical Content Detection](https://mlsanity.com/paper/2412.11745/) (2024)
- [SexWEs: Domain-Aware Word Embeddings via Cross-lingual Semantic Specialisation for Chinese Sexism Detection in Social Media](https://mlsanity.com/paper/2211.08447/) (2022)
- [A Feature Extraction based Model for Hate Speech Identification](https://mlsanity.com/paper/2201.04227/) (2022)
- [A Weakly Supervised Classifier and Dataset of White Supremacist Language](https://mlsanity.com/paper/2306.15732/) (2023)
- [Content-Localization based System for Analyzing Sentiment and Hate Behaviors in Low-Resource Dialectal Arabic: English to Levantine and Gulf](https://mlsanity.com/paper/2312.03727/) (2023)
- [EdgeAIGuard: Agentic LLMs for Minor Protection in Digital Spaces](https://mlsanity.com/paper/2503.00092/) (2025)
- [A Novel Plagiarism Detection Approach Combining BERT-based Word Embedding, Attention-based LSTMs and an Improved Differential Evolution Algorithm](https://mlsanity.com/paper/2305.02374/) (2023)
- [Leveraging Language Identification to Enhance Code-Mixed Text Classification](https://mlsanity.com/paper/2306.04964/) (2023)
- [Content-Localization based Neural Machine Translation for Informal Dialectal Arabic: Spanish/French to Levantine/Gulf Arabic](https://mlsanity.com/paper/2312.06926/) (2023)
- [Unveiling Social Media Comments with a Novel Named Entity Recognition System for Identity Groups](https://mlsanity.com/paper/2405.13011/) (2024)
- [Modeling offensive content detection for TikTok](https://mlsanity.com/paper/2408.16857/) (2024)
- [IMMENSE: Inductive Multi-perspective User Classification in Social Networks](https://mlsanity.com/paper/2608.05259/) (2026)

## Abstract (arXiv)

White supremacists embrace a radical ideology that considers white people superior to people of other races. The critical influence of these groups is no longer limited to social media; they also have a significant effect on society in many ways by promoting racial hatred and violence. White supremacist hate speech is one of the most recently observed harmful content on social media.Traditional channels of reporting hate speech have proved inadequate due to the tremendous explosion of information, and therefore, it is necessary to find an automatic way to detect such speech in a timely manner. This research investigates the viability of automatically detecting white supremacist hate speech on Twitter by using deep learning and natural language processing techniques. Through our experiments, we used two approaches, the first approach is by using domain-specific embeddings which are extracted from white supremacist corpus in order to catch the meaning of this white supremacist slang with bidirectional Long Short-Term Memory (LSTM) deep learning model, this approach reached a 0.74890 F1-score. The second approach is by using the one of the most recent language model which is BERT, BERT model provides the state of the art of most NLP tasks. It reached to a 0.79605 F1-score. Both approaches are tested on a balanced dataset given that our experiments were based on textual data only. The dataset was combined from dataset created from Twitter and a Stormfront dataset compiled from that white supremacist forum.