# MetaHate: A Dataset for Unifying Efforts on Hate Speech Detection

arXiv: https://arxiv.org/abs/2401.06526 · HTML: https://mlsanity.com/paper/2401.06526/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 82 references that are in the map.
- First posted on arXiv 12 Jan 2024.

## Builds on (most cited first, 27 of 27)

- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior](https://mlsanity.com/paper/1802.00393/) (2018)
- [Ex Machina: Personal Attacks Seen at Scale](https://mlsanity.com/paper/1610.08914/) (2016)
- [Hate Speech Dataset from a White Supremacy Forum](https://mlsanity.com/paper/1809.04444/) (2018)
- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [Directions in Abusive Language Training Data: Garbage In, Garbage Out](https://mlsanity.com/paper/2004.01670/) (2020)
- [Multilingual and Multi-Aspect Hate Speech Analysis](https://mlsanity.com/paper/1908.11049/) (2019)
- [A Benchmark Dataset for Learning to Intervene in Online Hate Speech](https://mlsanity.com/paper/1909.04251/) (2019)
- [Toxicity Detection: Does Context Really Matter?](https://mlsanity.com/paper/2006.00998/) (2020)
- [Measuring a hate speech spectrum with faceted Rasch item response theory and perspective-aware, explainable-by-design deep learning](https://mlsanity.com/paper/2009.10277/) (2020)
- [ETHOS: an Online Hate Speech Detection Dataset](https://mlsanity.com/paper/2006.08328/) (2020)
- [Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media](https://mlsanity.com/paper/1804.04257/) (2018)
- [Spread of hate speech in online social media](https://mlsanity.com/paper/1812.01693/) (2018)
- [Analyzing the Targets of Hate in Online Social Media](https://mlsanity.com/paper/1603.07709/) (2016)
- ["Call me sexist, but...": Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples](https://mlsanity.com/paper/2004.12764/) (2020)
- [HateMM: A Multi-Modal Dataset for Hate Video Classification](https://mlsanity.com/paper/2305.03915/) (2023)
- [Large-Scale Hate Speech Detection with Cross-Domain Transfer](https://mlsanity.com/paper/2203.01111/) (2022)
- [Aggression-annotated Corpus of Hindi-English Code-mixed Data](https://mlsanity.com/paper/1803.09402/) (2018)
- [Developing a Multilingual Annotated Corpus of Misogyny and Aggression](https://mlsanity.com/paper/2003.07428/) (2020)
- [Peer to Peer Hate: Hate Speech Instigators and Their Targets](https://mlsanity.com/paper/1804.04649/) (2018)
- [#MeTooMA: Multi-Aspect Annotations of Tweets Related to the MeToo Movement](https://mlsanity.com/paper/1912.06927/) (2019)
- [The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English](https://mlsanity.com/paper/1906.02045/) (2019)
- [Auditing Elon Musk's Impact on Hate Speech and Bots](https://mlsanity.com/paper/2304.04129/) (2023)

## Built on by (most cited first, 14 of 14)

- [Toxic Memes: A Survey of Computational Perspectives on the Detection and Explanation of Meme Toxicities](https://mlsanity.com/paper/2406.07353/) (2024)
- [Towards Efficient and Explainable Hate Speech Detection via Model Distillation](https://mlsanity.com/paper/2412.13698/) (2024)
- [Advancing Hate Speech Detection with Transformers: Insights from the MetaHate](https://mlsanity.com/paper/2508.04913/) (2025)
- [RL-Guided Data Selection for Language Model Finetuning](https://mlsanity.com/paper/2509.25850/) (2025)
- [Bridging Gaps in Hate Speech Detection: Meta-Collections and Benchmarks for Low-Resource Iberian Languages](https://mlsanity.com/paper/2510.11167/) (2025)
- [Benchmarking Open-Source Safety Guard Models: A Comprehensive Evaluation](https://mlsanity.com/paper/2605.28830/) (2026)
- [From BERT to Qwen: Hate Detection across architectures](https://mlsanity.com/paper/2507.10468/) (2025)
- [WATCHED: A Web AI Agent Tool for Combating Hate Speech by Expanding Data](https://mlsanity.com/paper/2509.01379/) (2025)
- [A Use-Case Specific Dataset for Measuring Dimensions of Responsible Performance in LLM-generated Text](https://mlsanity.com/paper/2510.20782/) (2025)
- [Efficient Hate Speech Detection: A Three-Layer LoRA-Tuned BERTweet Framework](https://mlsanity.com/paper/2511.06051/) (2025)
- [A Multi-Perspective Benchmark and Moderation Model for Evaluating Safety and Adversarial Robustness](https://mlsanity.com/paper/2601.03273/) (2025)
- [Do We Still Need Humans in the Loop? Human vs. LLM Annotation in Active Learning for TikTok Hate Speech Detection](https://mlsanity.com/paper/2604.13899/) (2026)
- [Against Political Polarization: A Unified Framework for Tracing Evolving Political Ideologies on Social Media](https://mlsanity.com/paper/2608.17987/) (2026)
- [An Explainable DistilBERT-BiLSTM-Attention Framework for Binary and Multi-Class Hate Speech Detection](https://mlsanity.com/paper/2609.28703/) (2026)

## Abstract (arXiv)

Hate speech represents a pervasive and detrimental form of online discourse, often manifested through an array of slurs, from hateful tweets to defamatory posts. As such speech proliferates, it connects people globally and poses significant social, psychological, and occasionally physical threats to targeted individuals and communities. Current computational linguistic approaches for tackling this phenomenon rely on labelled social media datasets for training. For unifying efforts, our study advances in the critical need for a comprehensive meta-collection, advocating for an extensive dataset to help counteract this problem effectively. We scrutinized over 60 datasets, selectively integrating those pertinent into MetaHate. This paper offers a detailed examination of existing collections, highlighting their strengths and limitations. Our findings contribute to a deeper understanding of the existing datasets, paving the way for training more robust and adaptable models. These enhanced models are essential for effectively combating the dynamic and complex nature of hate speech in the digital realm.