# Towards Hate Speech Detection at Large via Deep Generative Modeling

arXiv: https://arxiv.org/abs/2005.06370 · HTML: https://mlsanity.com/paper/2005.06370/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2020.

## Built on by (most cited first, 12 of 12)

- [Fight Fire with Fire: Fine-tuning Hate Detectors using Large Samples of Generated Hate Speech](https://mlsanity.com/paper/2109.00591/) (2021)
- [Character-level HyperNetworks for Hate Speech Detection](https://mlsanity.com/paper/2111.06336/) (2021)
- [Generative AI for Hate Speech Detection: Evaluation and Findings](https://mlsanity.com/paper/2311.09993/) (2023)
- [Down the Rabbit Hole: Detecting Online Extremism, Radicalisation, and Politicised Hate Speech](https://mlsanity.com/paper/2301.11579/) (2023)
- [Going Extreme: Comparative Analysis of Hate Speech in Parler and Gab](https://mlsanity.com/paper/2201.11770/) (2022)
- [Beyond Toxic: Toxicity Detection Datasets are Not Enough for Brand Safety](https://mlsanity.com/paper/2303.15110/) (2023)
- [Social World Knowledge: Modeling and Applications](https://mlsanity.com/paper/2306.16299/) (2023)
- [Hate Speech Detection Using Cross-Platform Social Media Data In English and German Language](https://mlsanity.com/paper/2410.05287/) (2024)
- [Evolving Hate Speech Online: An Adaptive Framework for Detection and Mitigation](https://mlsanity.com/paper/2502.10921/) (2025)
- [An Effective, Robust and Fairness-aware Hate Speech Detection Framework](https://mlsanity.com/paper/2409.17191/) (2024)
- [Reducing and Exploiting Data Augmentation Noise through Meta Reweighting Contrastive Learning for Text Classification](https://mlsanity.com/paper/2409.17474/) (2024)
- [Extreme Speech Classification in the Era of LLMs: Exploring Open-Source and Proprietary Models](https://mlsanity.com/paper/2502.15155/) (2025)

## Abstract (arXiv)

Hate speech detection is a critical problem in social media platforms, being often accused for enabling the spread of hatred and igniting physical violence. Hate speech detection requires overwhelming resources including high-performance computing for online posts and tweets monitoring as well as thousands of human experts for daily screening of suspected posts or tweets. Recently, Deep Learning (DL)-based solutions have been proposed for automatic detection of hate speech, using modest-sized training datasets of few thousands of hate speech sequences. While these methods perform well on the specific datasets, their ability to detect new hate speech sequences is limited and has not been investigated. Being a data-driven approach, it is well known that DL surpasses other methods whenever a scale-up in train dataset size and diversity is achieved. Therefore, we first present a dataset of 1 million realistic hate and non-hate sequences, produced by a deep generative language model. We further utilize the generated dataset to train a well-studied DL-based hate speech detector, and demonstrate consistent and significant performance improvements across five public hate speech datasets. Therefore, the proposed solution enables high sensitivity detection of a very large variety of hate speech sequences, paving the way to a fully automatic solution.