# An Investigation of Large Language Models for Real-World Hate Speech Detection

arXiv: https://arxiv.org/abs/2401.03346 · HTML: https://mlsanity.com/paper/2401.03346/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 38 references that are in the map.
- First posted on arXiv 7 Jan 2024.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes](https://mlsanity.com/paper/2005.04790/) (2020)
- [How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection](https://mlsanity.com/paper/2301.07597/) (2023)
- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://mlsanity.com/paper/2012.15761/) (2020)
- [Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis](https://mlsanity.com/paper/2304.04675/) (2023)
- [Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT](https://mlsanity.com/paper/2302.10198/) (2023)
- [Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media](https://mlsanity.com/paper/1804.04257/) (2018)
- [Detecting Hate Speech with GPT-3](https://mlsanity.com/paper/2103.12407/) (2021)
- ["Call me sexist, but...": Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples](https://mlsanity.com/paper/2004.12764/) (2020)
- [Hate Towards the Political Opponent: A Twitter Corpus Study of the 2020 US Elections on the Basis of Offensive Speech and Stance Detection](https://mlsanity.com/paper/2103.01664/) (2021)
- ["HOT" ChatGPT: The promise of ChatGPT in detecting and discriminating hateful, offensive, and toxic comments on social media](https://mlsanity.com/paper/2304.10619/) (2023)
- [SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection](https://mlsanity.com/paper/2108.03070/) (2021)
- [Racism is a Virus: Anti-Asian Hate and Counterspeech in Social Media during the COVID-19 Crisis](https://mlsanity.com/paper/2005.12423/) (2020)
- [You Only Prompt Once: On the Capabilities of Prompt Learning on Large Language Models to Tackle Toxic Content](https://mlsanity.com/paper/2308.05596/) (2023)
- [On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning](https://mlsanity.com/paper/2212.06573/) (2022)
- [Chain of Explanation: New Prompting Method to Generate Higher Quality Natural Language Explanation for Implicit Hate Speech](https://mlsanity.com/paper/2209.04889/) (2022)

## Built on by (most cited first, 20 of 20)

- [HateTinyLLM : Hate Speech Detection Using Tiny Large Language Models](https://mlsanity.com/paper/2405.01577/) (2024)
- [Echoes of Power: Investigating Geopolitical Bias in US and China Large Language Models](https://mlsanity.com/paper/2503.16679/) (2025)
- [Words Matter: Reducing Stigma in Online Conversations about Substance Use with Large Language Models](https://mlsanity.com/paper/2408.07873/) (2024)
- [Digital Guardians: Can GPT-4, Perspective API, and Moderation API reliably detect hate speech in reader comments of German online newspapers?](https://mlsanity.com/paper/2501.01256/) (2025)
- [Evaluation of Hate Speech Detection Using Large Language Models and Geographical Contextualization](https://mlsanity.com/paper/2502.19612/) (2025)
- [Gen-AI for User Safety: A Survey](https://mlsanity.com/paper/2411.06606/) (2024)
- [Hope vs. Hate: Understanding User Interactions with LGBTQ+ News Content in Mainstream US News Media through the Lens of Hope Speech](https://mlsanity.com/paper/2502.09004/) (2025)
- [Artificial Conversations, Real Results: Fostering Language Detection with Synthetic Data](https://mlsanity.com/paper/2503.24062/) (2025)
- [On Theoretically-Driven LLM Agents for Multi-Dimensional Discourse Analysis](https://mlsanity.com/paper/2602.13713/) (2026)
- [Safe Guard: an LLM-agent for Real-time Voice-based Hate Speech Detection in Social Virtual Reality](https://mlsanity.com/paper/2409.15623/) (2024)
- [ContextIQ: A Multimodal Expert-Based Video Retrieval System for Contextual Advertising](https://mlsanity.com/paper/2410.22233/) (2024)
- [MIMIC: Multimodal Islamophobic Meme Identification and Classification](https://mlsanity.com/paper/2412.00681/) (2024)
- [SMARTER: A Data-efficient Framework to Improve Toxicity Detection with Explanation via Self-augmenting Large Language Models](https://mlsanity.com/paper/2509.15174/) (2025)
- [LLM-based Few-Shot Early Rumor Detection with Imitation Agent](https://mlsanity.com/paper/2512.18352/) (2025)
- [LLMs-Integrated Automatic Hate Speech Recognition Using Controllable Text Generation Models](https://mlsanity.com/paper/2601.04654/) (2026)
- [Attention-Based Offline Reinforcement Learning and Clustering for Interpretable Sepsis Treatment](https://mlsanity.com/paper/2601.14228/) (2026)
- [Are Open-Weight LLMs Ready for Social Media Moderation? A Comparative Study on Bluesky](https://mlsanity.com/paper/2602.05189/) (2026)
- [QwenSafe: Multimodal Content Rating Description Identification via Preference-Aligned VLMs](https://mlsanity.com/paper/2605.20584/) (2026)
- [The Cross-Domain Generalization Cost of Offensive Language Detection](https://mlsanity.com/paper/2607.23512/) (2026)
- [From Specialization to Generalization: Instruction-tuned LLMs for Robust Harmful Content Mitigation](https://mlsanity.com/paper/2608.25605/) (2026)

## Abstract (arXiv)

Hate speech has emerged as a major problem plaguing our social spaces today. While there have been significant efforts to address this problem, existing methods are still significantly limited in effectively detecting hate speech online. A major limitation of existing methods is that hate speech detection is a highly contextual problem, and these methods cannot fully capture the context of hate speech to make accurate predictions. Recently, large language models (LLMs) have demonstrated state-of-the-art performance in several natural language tasks. LLMs have undergone extensive training using vast amounts of natural language data, enabling them to grasp intricate contextual details. Hence, they could be used as knowledge bases for context-aware hate speech detection. However, a fundamental problem with using LLMs to detect hate speech is that there are no studies on effectively prompting LLMs for context-aware hate speech detection. In this study, we conduct a large-scale study of hate speech detection, employing five established hate speech datasets. We discover that LLMs not only match but often surpass the performance of current benchmark machine learning models in identifying hate speech. By proposing four diverse prompting strategies that optimize the use of LLMs in detecting hate speech. Our study reveals that a meticulously crafted reasoning prompt can effectively capture the context of hate speech by fully utilizing the knowledge base in LLMs, significantly outperforming existing techniques. Furthermore, although LLMs can provide a rich knowledge base for the contextual detection of hate speech, suitable prompting strategies play a crucial role in effectively leveraging this knowledge base for efficient detection.