Data Refinement and Size Adjustment: The original publication reported 1,226,202 total instances and 1,101,165 public instances. Due to refined cross-dataset deduplication and overlapping instance resolution, the current dataset sizes are 1,226,203 (full) and 1,084,236 (public).
Expanded Metadata: Added dataset, source (platform), and split information to provide deeper context for each post.
Original ID Mapping: Included original_id to trace posts back to their raw source datasets where available.

Dataset Structure

The original dataset described in the accompanying paper contained 1,226,202 social media posts. Following the Version 2.0 refinement, the current total is 1,226,203 posts in a TSV file. Each element contains the following fields:

Field Name	Type	Possible Values	Description
id	str	any	Unique internal identifier for the MetaHate dataset.
original_id	str	any, null	Identifier from the original source dataset (if available).
label	int	0, 1	0 for non-hate speech, 1 for hate speech.
text	str	any	Social media post text. Each post is unique across the collection.
dataset	str	hateval_2019, etc.	The specific source dataset the post originates from.
source	str	twitter, reddit, etc.	The social media platform where the post was published.
split	str	train, test, mini-metahate-distil-train, mini-metahate-eval	MetaHate splits.

Source mapping

Dataset Key	Original Research Paper / Source
hate_offensive_2017	Davidson et al. (2017): Automated Hate Speech Detection and the Problem of Offensive Language
ENCASE_2018	Founta et al. (2018): Large Scale Crowdsourcing and Characterization of Twitter Abusive Behaviour
supremacist_2018	Gibert et al. (2018): Hate Speech Dataset from a White Supremacy Forum
online_harassment_2017	Golbeck et al. (2017): A Large Human-Labeled Corpus for Online Harassment Research
hate_speech_A_2016	Waseem (2016): Are You a Racist or Am I Seeing Things?
hate_speech_B_2016	Waseem and Hovy (2016): Hateful Symbols or Hateful People?
hateval_2019	Basile et al. (2019): SemEval-2019 Task 5
OLID_2019	Zampieri et al. (2019): Predicting the Type and Target of Offensive Posts in Social Media
TRAC1_2018	Kumar et al. (2018): Aggression-annotated Corpus of Hindi-English Code-mixed Data
TRAC2_2020	Bhattacharya et al. (2020): Developing a Multilingual Annotated Corpus of Misogyny and Aggression
#metooma_2020	Gautam et al. (2020): #MeTooMA: Multi-Aspect Annotations of Tweets
curated_hate_speech_2023	Mody, Huang, and de Oliveira (2023): A curated dataset for hate speech detection
hugging_face	Roshan Sharma and Ali Toosi (Hugging Face)
hatecomments_2023	Gupta, Priyadarshi, and Gupta (2023): Hateful Comment Detection and Hate Target-Type Prediction
kaggleA / B / etc.	Kaggle (Albright, SR, Jigsaw)
HASOC_2019	Mandl et al. (2019): Overview of the HASOC track at FIRE 2019
bullydetect_2018	Bin Abdur Rakib and Soon (2018): Using the Reddit Corpus for Cyberbully Detection
MLMA_2019	Ousidhoum et al. (2019): Multilingual and Multi-Aspect Hate Speech Analysis
hateful_tweets_2022	Albanyan and Blanco (2022): Pinpointing Fine-Grained Relationships between Hateful Tweets and Replies
multiclass_hatespeech_2022	Toraman, Şahinuç, and Yilmaz (2022): Large-Scale Hate Speech Detection with Cross-Domain Transfer
measuring_hate_speech_2020_2022	Kennedy et al. (2022) / Sachdeva et al. (2022): The Measuring Hate Speech Corpus
call_me_sexist_but_2021	Samory et al. (2020): The ‘Call me sexist, but’ sexism dataset
us_2020_elections	Grimminger and Klinger (2021): Hate Towards the Political Opponent
toxic_spans_2021	Pavlopoulos et al. (2021): SemEval-2021 Task 5
hatexplain_2020	Mathew et al. (2020): HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection
slur_corpus_2020	Kurrek, Saleem, and Ruths (2020): Towards a Comprehensive Taxonomy for Online Slur Usage
CAD_2021	Vidgen et al. (2021): Introducing CAD: the Contextual Abuse Dataset
gab_hate_corpus_2022	Kennedy et al. (2018): Introducing the Gab Hate Corpus
intervene_hate_2019	Qian et al. (2019): A Benchmark Dataset for Learning to Intervene in Online Hate Speech
exmachina_2016	Wulczyn, Thain, and Dixon (2016): Ex Machina: Personal Attacks Seen at Scale
ethos_2022	Mollas et al. (2020): ETHOS: an Online Hate Speech Detection Dataset
context_toxicity_2020	Pavlopoulos et al. (2020): Toxicity Detection: Does Context Really Matter?
hate_online_news_media_2018	Salminen et al. (2018): Anatomy of Online Hate
hate_speech_data_2017	Mondal et al. (2017) / Silva et al. (2016): A Measurement Study of Hate Speech
jigsaw_toxic	Jigsaw Toxic Comment Classification Challenge

Usage

In order to use MetaHate you need to agree to our Terms and Conditions. Access to the complete meta-collection (1,226,202) will be granted only upon the submission of all relevant agreements for the derived datasets. Otherwise, we will only provide the access to the publicly available datasets (1,101,165 instances).

To access the full data, we require the original Terms of Use of the following works:

Send these agreements to paloma.piot@udc.es to access the full data.

Disclaimer

This dataset includes content that may contain hate speech, offensive language, or other forms of inappropriate and objectionable material. The content present in the dataset is not created or endorsed by the authors or contributors of this project. It is collected from various sources and does not necessarily reflect the views or opinions of the project maintainers.

The purpose of using this dataset is for research, analysis, or educational purposes only. The authors do not endorse or promote any harmful, discriminatory, or offensive behaviour conveyed in the dataset.

Users are advised to exercise caution and sensitivity when interacting with or interpreting the dataset. If you choose to use the dataset, it is recommended to handle the content responsibly and in compliance with ethical guidelines and applicable laws.

The project maintainers disclaim any responsibility for the content within the dataset and cannot be held liable for how it is used or interpreted by others.

Citation

If you use this dataset, please cite the following reference:

@article{Piot_Martín-Rodilla_Parapar_2024,
  title={MetaHate: A Dataset for Unifying Efforts on Hate Speech Detection},
  volume={18},
  url={https://ojs.aaai.org/index.php/ICWSM/article/view/31445},
  DOI={10.1609/icwsm.v18i1.31445},
  abstractNote={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.},
  number={1},
  journal={Proceedings of the International AAAI Conference on Web and Social Media},
  author={Piot, Paloma and Martín-Rodilla, Patricia and Parapar, Javier},
  year={2024},
  month={May},
  pages={2025-2039}
}

Acknowledgements

The authors thank the funding from the Horizon Europe research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 101073351. The authors also thank the financial support supplied by the Consellería de Cultura, Educación, Formación Profesional e Universidades (accreditation 2019-2022 ED431G/01, ED431B 2022/33) and the European Regional Development Fund, which acknowledges the CITIC Research Center in ICT of the University of A Coruña as a Research Center of the Galician University System and the project PID2022-137061OB-C21 (Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, Proyectos de Generación de Conocimiento; supported by the European Regional Development Fund). The authors also thank the funding of project PLEC2021-007662 (MCIN/AEI/10.13039/501100011033, Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, Plan de Recuperación, Transformación y Resiliencia, Unión Europea-Next Generation EU).

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