# A Web of Hate: Tackling Hateful Speech in Online Social Spaces

arXiv: https://arxiv.org/abs/1709.10159 · HTML: https://mlsanity.com/paper/1709.10159/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2017.

## Built on by (most cited first, 15 of 15)

- [Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases](https://mlsanity.com/paper/2112.07868/) (2021)
- [You too Brutus! Trapping Hateful Users in Social Media: Challenges, Solutions & Insights](https://mlsanity.com/paper/2108.00524/) (2021)
- [MM-HSD: Multi-Modal Hate Speech Detection in Videos](https://mlsanity.com/paper/2508.20546/) (2025)
- [Human-in-the-Loop Hate Speech Classification in a Multilingual Context](https://mlsanity.com/paper/2212.02108/) (2022)
- [A Survey of Online Hate Speech through the Causal Lens](https://mlsanity.com/paper/2109.08120/) (2021)
- [Dimensions of Online Conflict: Towards Modeling Agonism](https://mlsanity.com/paper/2311.03584/) (2023)
- [A study of text representations in Hate Speech Detection](https://mlsanity.com/paper/2102.04521/) (2021)
- [Going Extreme: Comparative Analysis of Hate Speech in Parler and Gab](https://mlsanity.com/paper/2201.11770/) (2022)
- [AmpleHate: Amplifying the Attention for Versatile Implicit Hate Detection](https://mlsanity.com/paper/2505.19528/) (2025)
- [Emoji-Based Transfer Learning for Sentiment Tasks](https://mlsanity.com/paper/2102.06423/) (2021)
- [Placing M-Phasis on the Plurality of Hate: A Feature-Based Corpus of Hate Online](https://mlsanity.com/paper/2204.13400/) (2022)
- [AggregHate: An Efficient Aggregative Approach for the Detection of Hatemongers on Social Platforms](https://mlsanity.com/paper/2409.14464/) (2024)
- [HateGPT: Unleashing GPT-3.5 Turbo to Combat Hate Speech on X](https://mlsanity.com/paper/2411.09214/) (2024)
- [Social Hatred: Efficient Multimodal Detection of Hatemongers](https://mlsanity.com/paper/2506.19603/) (2025)
- [Leveraging the Potential of Prompt Engineering for Hate Speech Detection in Low-Resource Languages](https://mlsanity.com/paper/2506.23930/) (2025)

## Abstract (arXiv)

Online social platforms are beset with hateful speech - content that expresses hatred for a person or group of people. Such content can frighten, intimidate, or silence platform users, and some of it can inspire other users to commit violence. Despite widespread recognition of the problems posed by such content, reliable solutions even for detecting hateful speech are lacking. In the present work, we establish why keyword-based methods are insufficient for detection. We then propose an approach to detecting hateful speech that uses content produced by self-identifying hateful communities as training data. Our approach bypasses the expensive annotation process often required to train keyword systems and performs well across several established platforms, making substantial improvements over current state-of-the-art approaches.