HateAtlas

A target-aware atlas of English hate-speech datasets, with each row's provenance kept. HateAtlas maps 21 published datasets onto one schema: a hate level, the groups a text targets, and every source's own label, id and author beside each text. The configurations published here hold 18 of them, as their licences allow, and the builder adds the rest. Its default configuration holds 703,992 unique texts, 91,091 of them aimed at a racial or ethnic group (28,353 hateful). A builder that anyone can rerun fetches every source from its home, checks it against the numbers its authors published, cleans it, removes personal data and merges duplicates.

Content warning. The data contains hateful, racist and abusive language, quoted as posted apart from personal data.

Why it exists

HateAtlas began as a racism-detection dissertation for the MSc in Big Data Analytics and Artificial Intelligence at Letterkenny Institute of Technology (now part of the Atlantic Technological University), 2021. Its models, BERT-large read by CNN and BiLSTM layers and fused with features of each tweet, learned racism from the annotations of Waseem and Hovy (2016), and the dissertation concluded that new datasets were needed. A 2026 rebuild showed why. The racist tweets were written by nine users, one of whom wrote 96 to 99% of them, mostly about Islam (Arango et al., 2019; Wiegand et al., 2019). Every competent model reached the same ceiling, and the models missed hand-written racist sentences aimed at Asian and Bengali people, which the data did not cover; only one flagged the sentence aimed at Black people, just over its threshold. They had learned the authors and their topic. Once no author appears in both training and test data, hate F1 on that dataset falls to between 7 and 24 (Arango et al., 2019). Released as tweet IDs, much of it had been deleted by 2021.

What racism research lacks is not one more dataset but the existing ones read together. Racism is annotated, under different names and schemes, across many datasets, platforms and years. MetaHate (Piot et al., 2024) unified 36 of them as binary hate labels, without targets, authors or provenance. HateAtlas keeps what that leaves out:

• Targets. Each text's targeted groups come from a controlled vocabulary, so racism is a filter rather than a separate dataset, and models can be tested group by group.
• Breadth. In the default configuration, 28,353 hateful texts target 14 racial or ethnic groups, drawn from 11 sources on 6 platforms; the sources that name authors alone contribute 5,304 distinct authors of them.
• Provenance. Each source's own label and id stay on every row, so any source's definition can be applied and any source dropped.
• Leakage control. Exact duplicates are merged, near-duplicates grouped and authors kept as pseudonyms, so an evaluation can hold out texts, authors or whole sources.
• Text that lasts. Only sources that release their text are included, so nothing decays as posts are deleted.

Configurations

Configuration	Licence	Sources	Texts	Hateful	Offensive	Neither	Not hateful	Unknown	Race or ethnicity target	Hateful, race or ethnicity target
open	CC-BY-4.0	12	703,992	74,513	61,335	501,333	31,119	35,692	91,091	28,353
share_alike	CC-BY-SA-4.0	15	771,955	76,142	79,810	528,864	37,497	49,642	91,851	28,746
non_commercial	CC-BY-NC-SA-4.0	15	768,479	99,135	61,345	502,541	69,295	36,163	91,091	28,351

Each configuration is built only from sources whose licences allow its own, since share-alike and non-commercial material cannot be combined in one table. ETHOS (GPL-3.0), OLID (unspecified) and HASOC 2019 (unspecified) can be added only by running the builder, which also builds a table of every source for local research.

The same tables, with their build reports and this card, are on Kaggle and in the repository's releases, as hateatlas-open.parquet, hateatlas-share_alike.parquet and hateatlas-non_commercial.parquet.

Quick start

from datasets import load_dataset

atlas = load_dataset("JoshPola96/HateAtlas", "open", split="train").to_pandas()
racism = atlas[atlas.target_category.apply(lambda categories: "race_ethnicity" in categories)]
hateful_racism = racism[racism.hate == "hateful"]

To evaluate, split by near_duplicate_group (for example with scikit-learn's GroupShuffleSplit), so that near-identical texts never fall on both sides; to test generalisation, hold out a source, or the authors that provenance names. A word-count model trained on the other sources stays well above chance on each held-out source but loses ground, most where a source's hate is specific (East Asian Prejudice, the Slur Corpus), so a score on mixed sources overstates performance on new ones. A model's input is text alone: provenance, soft_label, conflict, n_annotators and the target fields describe the labels, and sources, licences, platform and text_form identify sources whose share of hate differs, so feeding any of them to a model leaks the answer.

Racism in the default configuration

Group	Texts	Hateful	Sources of the hateful texts
black	40,799	14,732	Contextual Abuse Dataset, Davidson et al., Gab Hate Corpus, HateXplain, Implicit Hate, Jigsaw Unintended Bias, MLMA, Measuring Hate Speech, Slur Corpus and Social Bias Frames
white	29,961	4,197	Contextual Abuse Dataset, Davidson et al., Gab Hate Corpus, HateXplain, Implicit Hate, Jigsaw Unintended Bias, Measuring Hate Speech and Social Bias Frames
jewish	12,806	3,884	Contextual Abuse Dataset, Davidson et al., Gab Hate Corpus, HateXplain, Implicit Hate, Jigsaw Unintended Bias, MLMA, Measuring Hate Speech and Social Bias Frames
latinx	3,601	967	Contextual Abuse Dataset, Davidson et al., HateXplain, Implicit Hate, Jigsaw Unintended Bias, MLMA, Measuring Hate Speech and Social Bias Frames
asian	5,701	791	Contextual Abuse Dataset, Davidson et al., East Asian Prejudice, HateXplain, Implicit Hate, Jigsaw Unintended Bias, MLMA, Measuring Hate Speech and Social Bias Frames
ethnic_minorities	754	731	Contextual Abuse Dataset, Gab Hate Corpus and Implicit Hate
arab	900	611	Contextual Abuse Dataset, Davidson et al., HateXplain, Implicit Hate, MLMA and Social Bias Frames
east_asian	604	437	East Asian Prejudice
non_white	422	412	Contextual Abuse Dataset, Implicit Hate and Social Bias Frames
middle_eastern	1,369	401	Measuring Hate Speech
indigenous	539	185	Contextual Abuse Dataset, Implicit Hate, Measuring Hate Speech and Social Bias Frames
pacific_islander	320	46	Measuring Hate Speech
romani	14	14	Contextual Abuse Dataset
mixed_race	8	8	Contextual Abuse Dataset

Fields

Field	Type	Meaning
id	string	A hash of the text's normalised form, the same in every configuration and build
near_duplicate_group	string	Shared by texts whose word sets overlap by at least 0.8 (Jaccard), directly or through others; split by it to avoid leakage
text	string	The text as a reader sees it, with personal data replaced
text_form	string	raw as posted, or preprocessed where the source lower-cased, tokenised or replaced parts of it
platform	string	twitter, reddit, gab, youtube, stormfront, fox_news, civil_comments or unknown
hate	string	The hate level that all the text's sources allow (below)
conflict	bool	True where its sources disagree; hate is then unknown
target_category	list of strings	race_ethnicity, religion, national_origin, gender, sexual_orientation, disability, age, political or other
target_group	list of strings	Groups from a controlled vocabulary, such as black, asian, jewish, latinx, muslim and women
explicitness	string	explicit, implicit or unknown, where a source annotates it for hostile texts
soft_label	struct	The share of annotators choosing hateful, offensive and neither, pooled over the sources that release votes
n_annotators	int	The annotators behind the label
sources	list of strings	The sources holding the text
licences	list of strings	Their licences
provenance	list of structs	Per source: source, source_id, its mapped hate, its own label verbatim (label_raw) and a pseudonymous author_id where it names authors

Hate levels

Level	Meaning
hateful	The source's annotators judged it hate: an attack on people for who they are
offensive	Abusive, insulting or vulgar, but not hate
neither	Neither hateful nor offensive
not_hateful	Not hate, where the source does not say whether it is offensive
unknown	The source's annotators did not settle it, or its sources disagree

Sources define hate and offence differently (Fortuna et al., 2020), so each source's mapping is set out below and provenance keeps its own label. Two differences matter most: some sources count casual profanity as offensive and others as neither, and some count attacks on political opinion as hate (HASOC 2019) or any offensive post about a group (Social Bias Frames).

Sources

Source	Licence	Configurations	Texts	Hateful
HateXplain	CC-BY-4.0	open, share_alike, non_commercial	20,148	5,935
Gab Hate Corpus	CC-BY-4.0	open, share_alike, non_commercial	27,310	2,319
Measuring Hate Speech	CC-BY-4.0	open, share_alike, non_commercial	39,563	10,353
Contextual Abuse Dataset	CC-BY-4.0	open, share_alike, non_commercial	23,223	2,290
Social Bias Frames	CC-BY-4.0	open, share_alike, non_commercial	44,669	14,894
East Asian Prejudice	CC-BY-4.0	open, share_alike, non_commercial	19,997	2,660
MLMA	MIT	open, share_alike, non_commercial	5,643	897
ETHOS	GPL-3.0	builder only
Implicit Hate	CC-BY-4.0	open, share_alike, non_commercial	21,480	8,189
Jigsaw Unintended Bias	CC0-1.0	open, share_alike, non_commercial	447,707	7,527
Davidson et al.	MIT	open, share_alike, non_commercial	24,783	1,422
Slur Corpus	MIT	open, share_alike, non_commercial	39,987	20,530
IMSyPP English YouTube corpus	CC-BY-SA-4.0	share_alike	62,388	1,423
White Supremacy Forum	CC-BY-SA-3.0-ES	share_alike	10,894	1,196
Learning to Intervene	CC-BY-NC-4.0	non_commercial	50,963	18,937
Fox News comments	MIT	open, share_alike, non_commercial	1,526	435
CREHate	CC-BY-SA-4.0	share_alike	1,580	709
OLID	unspecified	builder only
HatEval	CC-BY-NC-4.0	non_commercial	12,957	5,460
"Call me sexist, but"	CC-BY-NC-SA-4.0	non_commercial	2,634	1,234
HASOC 2019	unspecified	builder only

How each source was read and mapped

HateXplain · CC-BY-4.0

HateXplain (Mathew et al., 2021): 20,148 Twitter and Gab posts, each labelled by three annotators as hate speech, offensive or normal, with the groups each annotator saw targeted. Fetched from the authors' repository (CC BY 4.0). The text is the authors' tokens, lower-cased, with placeholders such as <user> and <number>.

Cite: Mathew, B., Saha, P., Yimam, S. M., Biemann, C., Goyal, P. and Mukherjee, A. (2021). HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection. AAAI 2021. https://doi.org/10.1609/aaai.v35i17.17745

Gab Hate Corpus · CC-BY-4.0

Gab Hate Corpus (Kennedy et al., 2022): 27,665 Gab posts with 86,529 annotations, one row per annotator, from the authors' OSF project (CC BY 4.0). Each annotator marks hate (an assault on human dignity or a call for violence) and, independently, vulgar or offensive language; targets only when they marked hate. So targets exist only on posts a majority called hateful, and the corpus holds no non-hateful posts that mention a group. Explicitness is left unknown: the authors report too little agreement on it to use. GHC names target categories only, never finer groups.

Cite: Kennedy, B. et al. (2022). Introducing the Gab Hate Corpus: Defining and Applying Hate-Based Rhetoric to Social Media Posts at Scale. Language Resources and Evaluation. https://doi.org/10.1007/s10579-021-09569-x

Measuring Hate Speech · CC-BY-4.0

Measuring Hate Speech (Kennedy et al., 2020; Sachdeva et al., 2022): 39,565 Reddit, Twitter and YouTube comments with 135,556 annotations by 7,912 annotators, from the Hugging Face Hub (CC BY 4.0). The hate level comes from the authors' hate speech score, a Rasch measure adjusted for each annotator's severity: above 0.5 is hateful, below -1 is counter or supportive speech (neither), and the band between, which the authors call neutral or ambiguous, is unknown, since the corpus does not separate offensive from neutral. Every annotator was asked for targets, so targets are kept by majority. No soft label: the annotators answered survey items, not hate levels.

Cite: Sachdeva, P., Barreto, R., Bacon, G., Sahn, A., von Vacano, C. and Kennedy, C. J. (2022). The Measuring Hate Speech Corpus: Leveraging Rasch Measurement Theory for Data Perspectivism. NLPerspectives @ LREC 2022. https://aclanthology.org/2022.nlperspectives-1.11; Kennedy, C. J., Bacon, G., Sahn, A. and von Vacano, C. (2020). Measuring a Hate Speech Spectrum with Faceted Rasch Item Response Theory and Perspective-Aware, Explainable-by-Design Deep Learning. https://arxiv.org/abs/2009.10277

Contextual Abuse Dataset · CC-BY-4.0

Contextual Abuse Dataset (Vidgen et al., 2021): 26,550 Reddit entries with 27,494 expert-adjudicated labels, one row per label, from Zenodo (CC BY 4.0). Keeps the 23,417 entries the authors used, leaving out those they excluded as empty, bot, non-English or image-only. CAD's taxonomy sets the level: identity-directed abuse is hateful, abuse of an affiliation (a party, a profession) or of a person is offensive, neutral and counter speech are neither, and non-hateful use of a slur, which spans reclaimed use and quotation that keeps the slur's force, is not hateful, as in the Slur Corpus; an entry with several labels takes the most severe. Targets are the annotators' free text, so only the common ones map to groups. Usernames become keyed pseudonyms, which pseudonymise rather than anonymise them. No vote counts or soft label: disagreements were adjudicated.

Cite: Vidgen, B., Nguyen, D., Margetts, H., Rossini, P. and Tromble, R. (2021). Introducing CAD: the Contextual Abuse Dataset. NAACL-HLT 2021. https://aclanthology.org/2021.naacl-main.182

Social Bias Frames · CC-BY-4.0

Social Bias Frames (Sap et al., 2020): 44,671 posts from Reddit, Twitter, Gab and Stormfront with 147,139 annotation rows, from the authors' site (CC BY 4.0). SBF asks whether a post could offend anyone and whether it targets a group or an individual, never whether it is hate. So a post is hateful when a majority find it offensive and a majority of those asked say it targets a group, offensive when they say an individual, neither when a majority find it inoffensive, and unknown on a tie; a "maybe" abstains, which makes the offensive vote the authors' mean-above-0.5 rule. Targets are kept on hateful posts, by majority of the annotators who named a group. A post is its text, as in the authors' own aggregation: one text can sit under several HIT IDs, and 203 texts sit in more than one of the authors' splits. Two origins reached SBF tokenised, so their texts count as preprocessed: Stormfront (the White Supremacy Forum corpus) and r/meanjokes, also lower-cased with tags such as <number> and <repeat>.

Cite: Sap, M., Gabriel, S., Qin, L., Jurafsky, D., Smith, N. A. and Choi, Y. (2020). Social Bias Frames: Reasoning about Social and Power Implications of Language. ACL 2020. https://aclanthology.org/2020.acl-main.486

East Asian Prejudice · CC-BY-4.0

East Asian Prejudice (Vidgen et al., 2020): 20,000 COVID-19-era tweets, each labelled by two annotators and settled by an expert, from Zenodo (CC BY 4.0). The expert's label sets the level. Its "entity-directed hostility" covers East Asian identities and institutions alike, so, as CAD separates them, hostility at people or places is hateful and hostility only at institutions, office-holders or professions is offensive; criticism ("critical, but not abusive"), counter speech, discussion of prejudice and none of the above are neither. Targets were asked only for hostility and criticism. Usernames were anonymised by the authors, who also lower-cased about half the texts (58% hold no capital, against 7% of Davidson's tweets, and 1,441 retweets begin "rt", which Twitter never writes), so the texts count as preprocessed.

Cite: Vidgen, B., Botelho, A., Broniatowski, D., Guest, E., Hall, M., Margetts, H., Tromble, R., Waseem, Z. and Hale, S. (2020). Detecting East Asian Prejudice on Social Media. Fourth Workshop on Online Abuse and Harms. https://aclanthology.org/2020.alw-1.19

MLMA · MIT

MLMA (Ousidhoum et al., 2019), English part: 5,647 tweets from the authors' GitHub (MIT). Each tweet carries the hostility types at least two annotators agreed on. Hateful when one is "hateful", offensive for any other hostility type, neither when the only type is "normal", and unknown when "normal" sits beside a hostility type, since the annotators then disagreed whether the tweet is hostile at all. The authors' directness label is their explicitness (direct is explicit), given for hostile tweets. Targets were asked for every tweet. The authors replaced mentions and links with @user and @URL, and stored each tweet JSON-escaped with its double quotes deleted; the escapes are decoded and the quotes put back. The per-annotator labels are lost, so no soft label.

Cite: Ousidhoum, N., Lin, Z., Zhang, H., Song, Y. and Yeung, D.-Y. (2019). Multilingual and Multi-Aspect Hate Speech Analysis. EMNLP-IJCNLP 2019. https://aclanthology.org/D19-1474

ETHOS · GPL-3.0

ETHOS (Mollas et al., 2022): 998 YouTube and Reddit comments, each with the share of annotators who judged it hate speech, and target categories for the 433 the authors count as hate, from the authors' GitHub (GPL-3.0). Hateful when that share is above one half, unknown at exactly one half (the authors count it as hate), and not hateful below it, since ETHOS asks only about hate and so does not separate offensive from neutral. Targets are kept when more than half the annotators named them. The platform of each comment is not given, and the annotators' count varies and is not released.

Cite: Mollas, I., Chrysopoulou, Z., Karlos, S. and Tsoumakas, G. (2022). ETHOS: a multi-label hate speech detection dataset. Complex & Intelligent Systems. https://doi.org/10.1007/s40747-021-00608-2

Implicit Hate · CC-BY-4.0

Implicit Hate (ElSherief et al., 2021): 21,480 posts, tweets from US extremist accounts plus 548 taken from Social Bias Frames, each labelled explicit hate, implicit hate or not hate, with a free-text target for the implicit ones (CC BY 4.0). The authors ask users to complete a survey before downloading, so the corpus zip is fetched by hand. Both kinds of hate are hateful, with that explicitness; not hate is not hateful, since the corpus does not separate offensive from neutral. The commonest target strings map to groups. The text is the authors' lower-cased, pre-processed form. The README inside the zip counts 22,056 tweets, 576 more than its files hold.

Cite: ElSherief, M., Ziems, C., Muchlinski, D., Anupindi, V., Seybolt, J., De Choudhury, M. and Yang, D. (2021). Latent Hatred: A Benchmark for Understanding Implicit Hate Speech. EMNLP 2021. https://aclanthology.org/2021.emnlp-main.29

Jigsaw Unintended Bias · CC0-1.0

Jigsaw Unintended Bias (Civil Comments, 2019): of 1,999,516 news-site comments, the 448,000 whose identity mentions were annotated, from the Kaggle competition, downloaded by hand once its rules are accepted (CC0, text included). Each score is the share of annotators who said yes. Hateful when more than half call the comment an identity attack, else offensive when more than half call it toxic, neither when fewer than half say either, and unknown at exactly half. Identities more than half the annotators saw mentioned are kept as targets, at every level. The other comments were never annotated for identities, so a blank there means "not asked", and they are left out.

Cite: Borkan, D., Dixon, L., Sorensen, J., Thain, N. and Vasserman, L. (2019). Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification. WWW '19 Companion. https://doi.org/10.1145/3308560.3317593

Davidson et al. · MIT

Davidson et al. (2017): 24,783 tweets, each labelled by three or more annotators as hate speech, offensive language or neither, with every vote released, from the authors' GitHub (MIT). The level is the strict majority of votes (unknown for the 60 tweets with only a plurality) and the soft label is the votes' shares. No targets. The release is not the paper's sample: the paper describes 24,802 tweets, 1.3% of them unanimously hate, where the file holds 24,783 and 1.06%.

Cite: Davidson, T., Warmsley, D., Macy, M. and Weber, I. (2017). Automated Hate Speech Detection and the Problem of Offensive Language. ICWSM 2017. https://doi.org/10.1609/icwsm.v11i1.14955

Slur Corpus · MIT

Slur Corpus (Kurrek et al., 2020): 40,000 Reddit comments, each using one of three slurs (against Black, gay and transgender people), labelled by two annotators for how the slur is used, from the authors' GitHub (MIT). Derogatory use is hateful; reclaimed use and homonyms are neither; the "non-derogatory, non-appropriative" use (discussing the word, counter speech, sarcasm), which keeps the slur's force, is not hateful; noise is unknown. Every use but a homonym targets the slur's group. Authors are pseudonymised as for CAD, so one Reddit user has one id across both.

Cite: Kurrek, J., Saleem, H. M. and Ruths, D. (2020). Towards a Comprehensive Taxonomy and Large-Scale Annotated Corpus for Online Slur Usage. Fourth Workshop on Online Abuse and Harms. https://aclanthology.org/2020.alw-1.17

IMSyPP English YouTube corpus · CC-BY-SA-4.0

IMSyPP English YouTube corpus (2021): 62,414 comments on Covid-19 videos, from CLARIN.SI (CC BY-SA 4.0), labelled appropriate, inappropriate, offensive or violent, with a target for offensive and violent ones. The 51,655 training comments have two annotations each; the 10,759 evaluation comments four, two made with the conversation's context and two without. Per annotation, offensive or violent speech at a protected group (racism, migrants, islamophobia, antisemitism, religion, sexism, homophobia) is hateful, at politics, media, an individual or others offensive, and inappropriate (vulgar, untargeted) speech offensive. A comment takes the strict majority of its annotations, unknown on a tie; an annotation without a type abstains. Targets are kept by majority of the annotators who gave one. The context file marks replies with a "---*- " prefix, removed here. A spreadsheet turned comments starting with "+" into "#ERROR! (Formula parse error.)" in some files: annotations of that error abstain, the comment's real text is used where another file kept it, and the 4 comments that survive only as the error are left out. Where versions of a comment differ (13, by truncation or editing), the longest is kept.

Cite: Ljubešić, N., Mozetič, I., Cinelli, M. and Kralj Novak, P. (2021). English YouTube Hate Speech Corpus. CLARIN.SI. http://hdl.handle.net/11356/1454

White Supremacy Forum · CC-BY-SA-3.0-ES

White Supremacy Forum (de Gibert et al., 2018): 10,944 sentences from Stormfront posts by 2,792 users, labelled hate, no hate, relation (hateful only together with other sentences of the post) or idk/skip (not English, or too little to judge), from the authors' GitHub (CC BY-SA 3.0 Spain). Hate is hateful; no hate is not hateful, since the dataset does not separate offensive from neutral; relation and idk/skip are unknown for a sentence read alone. No targets. The text is the authors' tokenised form, with spaces around punctuation. Users are pseudonymised into author ids.

Cite: de Gibert, O., Perez, N., García-Pablos, A. and Cuadros, M. (2018). Hate Speech Dataset from a White Supremacy Forum. Second Workshop on Abusive Language Online. https://aclanthology.org/W18-5102

Learning to Intervene · CC-BY-NC-4.0

Learning to Intervene (Qian et al., 2019): Gab and Reddit conversations whose posts are labelled hate speech or not, from the authors' GitHub (CC BY-NC 4.0). Conversations overlap, so a post can appear in several: it is hateful when every appearance is labelled hate, not hateful (the dataset does not separate offensive from neutral) when none is, and unknown when they disagree (257 posts). The written intervention responses are not included. No targets. The Reddit file holds 15 fewer comments than the paper reports, and 2 more labelled hate.

Cite: Qian, J., Bethke, A., Liu, Y., Belding, E. and Wang, W. Y. (2019). A Benchmark Dataset for Learning to Intervene in Online Hate Speech. EMNLP-IJCNLP 2019. https://aclanthology.org/D19-1482

Fox News comments · MIT

Fox News comments (Gao and Huang, 2017): 1,528 comments from 10 discussion threads on Fox News articles, labelled hateful or not, from the authors' GitHub (MIT). Hateful is hateful; the rest is not hateful, since the dataset does not separate offensive from neutral. No targets. Each line names its commenter, pseudonymised into an author id.

Cite: Gao, L. and Huang, R. (2017). Detecting Online Hate Speech Using Context Aware Models. RANLP 2017. https://aclanthology.org/R17-1036

CREHate · CC-BY-SA-4.0

CREHate (Lee et al., 2024): 1,580 posts, 980 re-annotated from Social Bias Frames and 600 collected from Reddit and YouTube in Australia, the United Kingdom, Singapore and South Africa, each labelled hate or not by annotators from those four countries and the United States, from the Hugging Face Hub (CC BY-SA 4.0). Each country's label is the majority of its annotators, "not sure" abstaining; each country then has one vote, so a post is hateful when three or more countries call it hate and not hateful otherwise (the dataset asks only about hate). The five country labels are kept as the raw label. No targets. The re-annotated posts carry no platform here; their Social Bias Frames copies do.

Cite: Lee, N., Jung, C., Myung, J., Jin, J., Camacho-Collados, J., Kim, J. and Oh, A. (2024). Exploring Cross-Cultural Differences in English Hate Speech Annotations: From Dataset Construction to Analysis. NAACL 2024. https://doi.org/10.18653/v1/2024.naacl-long.236

OLID · unspecified

OLID (Zampieri et al., 2019) with AbuseEval (Caselli et al., 2020): 14,100 tweets labelled offensive or not, then targeted or not, then at an individual, a group or other, with AbuseEval's later abusive (explicit or implicit) or not abusive label on each. A tweet is hateful when it is an offence targeted at a group that AbuseEval finds abusive, offensive when otherwise offensive, and neither when not offensive; explicitness is AbuseEval's reading of the offence. Only "other" targets name a category. OLID states no licence, and its official page now requires a sign-in, so its files come from a Hugging Face mirror, checked against the paper's counts; AbuseEval's labels are CC BY-NC-SA 4.0. OLID replaced mentions with @USER and links with URL.

Cite: Zampieri, M., Malmasi, S., Nakov, P., Rosenthal, S., Farra, N. and Kumar, R. (2019). Predicting the Type and Target of Offensive Posts in Social Media. NAACL-HLT 2019. https://aclanthology.org/N19-1144; Caselli, T., Basile, V., Mitrović, J., Kartoziya, I. and Granitzer, M. (2020). I Feel Offended, Don't Be Abusive! Implicit/Explicit Messages in Offensive and Abusive Language. LREC 2020. https://aclanthology.org/2020.lrec-1.760

HatEval · CC-BY-NC-4.0

HatEval (Basile et al., 2019), English part: 12,971 tweets collected about immigrants or women, labelled for hate, for whether hate targets a group or an individual, and for aggression, from the organisers' gated Hugging Face dataset (CC BY-NC 4.0). Hate is hateful; the rest is not hateful, since HatEval asks only about hate. Each tweet keeps the group it was collected about as its target. The release's test set holds 2,971 English tweets where the paper describes 3,000.

Cite: Basile, V., Bosco, C., Fersini, E., Nozza, D., Patti, V., Rangel Pardo, F. M., Rosso, P. and Sanguinetti, M. (2019). SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter. SemEval 2019. https://aclanthology.org/S19-2007

"Call me sexist, but" · CC-BY-NC-SA-4.0

"Call me sexist, but" (Samory et al., 2021): the 2,636 tweets its own crowdworkers annotated, five each, for sexist content and for sexist, uncivil or civil phrasing: tweets beginning "call me sexist, but", and the benevolent and hostile sexism tweets of Jha and Mamidi (2017) and Waseem and Hovy (2016), from GESIS after registration (CC BY-NC-SA 4.0). Psychology scale items and the adversarial rewrites are left out, as is the "other" set, whose labels are inherited rather than annotated. Per annotation, sexist content or sexist phrasing (the authors' rule) is hateful, uncivil but non-sexist phrasing offensive, and the rest neither; a tweet takes the strict majority, unknown without one. Sexist tweets target gender; their explicitness is our reading of the codebook: sexist phrasing is explicit, sexism in content alone implicit. Mentions are anonymised by the authors.

Cite: Samory, M., Sen, I., Kohne, J., Flöck, F. and Wagner, C. (2021). “Call me sexist, but...”: Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples. ICWSM 2021. https://doi.org/10.1609/icwsm.v15i1.18085; Samory, M. (2021). The 'Call me sexist but' Dataset (CMSB). GESIS. https://doi.org/10.7802/2251

HASOC 2019 · unspecified

HASOC 2019 (Mandl et al., 2019), English: 7,005 posts from Twitter and partly Facebook (each post's platform is not given), labelled hate or offensive or neither, then hate, offensive or profane, from the organisers' site, which states no licence. Hate is hateful; offensive and profane are offensive; the rest is neither. No targets. The training and test files reuse ids for different posts, so each id carries its split. The overview paper's training table swaps the offensive and profane counts against the file (667 and 451); both map to offensive. Some training posts hold line breaks and apostrophes as escapes, which are decoded. The 2020 and 2021 English sets are password-protected and not yet included.

Cite: Mandl, T., Modha, S., Majumder, P., Patel, D., Dave, M., Mandlia, C. and Patel, A. (2019). Overview of the HASOC track at FIRE 2019: Hate Speech and Offensive Content Identification in Indo-European Languages. FIRE 2019. https://doi.org/10.1145/3368567.3368584

How it was built

Each source is fetched from where its authors publish it, mapped to the schema and checked against the numbers in its paper or card; a source that does not reproduce them stops the build. Texts are cleaned to what a reader sees (HTML entities, mis-decoded characters and sources' own escaping undone, invisible characters removed, Unicode composed), personal data is replaced, and rows are dropped that hold nothing to classify (no letter, digit or emoji once mentions, links and placeholders are removed), only a removal notice, or a bot's message (signed "I am a bot"; people mocking bots are kept). Texts that match once case, mentions, links, placeholders, punctuation and a retweet's "RT" are removed become one row, with the level that all its sources allow. Near-duplicates are not merged, since many are different posts (a joke retold about another group differs by one word), but share a near_duplicate_group. A quality gate checks every table before it is written, an independent audit recomputes every value from the published table, and two builds from the same inputs are byte-identical.

Known limitations

• Definitions differ across sources (see Hate levels). Where a definition matters, use each source's own label from provenance. Where two sources hold the same text, they agree on whether it is hateful as follows. Cohen's κ corrects agreement for chance: when one source almost never says hateful, κ falls towards zero however high the raw agreement, the kappa paradox, so both are given:

Sources	Shared texts	Agree on hateful or not	Cohen's κ	Hateful share, each
Social Bias Frames · White Supremacy Forum	3,725	84.3%	0.52	25% · 16%
Davidson et al. · Social Bias Frames	2,580	56.2%	0.09	9% · 48%
CREHate · Social Bias Frames	911	79.8%	0.59	53% · 68%
Implicit Hate · Social Bias Frames	538	99.8%	0.80	100% · 99%
Learning to Intervene · Jigsaw Unintended Bias	268	97.8%	0.39	3% · 1%
Contextual Abuse Dataset · Jigsaw Unintended Bias	182	96.2%	0.21	4% · 1%
Contextual Abuse Dataset · Learning to Intervene	173	97.1%	0.75	7% · 5%
CREHate · Implicit Hate	139	84.9%	0.00	85% · 100%
Gab Hate Corpus · Jigsaw Unintended Bias	132	96.2%	0.00	4% · 0%
Gab Hate Corpus · Implicit Hate	120	75.0%	0.09	73% · 98%
Gab Hate Corpus · Learning to Intervene	119	90.8%	0.66	17% · 16%
Jigsaw Unintended Bias · White Supremacy Forum	117	100.0%	undefined	0% · 0%
IMSyPP English YouTube corpus · Jigsaw Unintended Bias	109	100.0%	undefined	0% · 0%

• Label reliability differs. Where a source labels the same text twice, the two labels differ this often. A low rate is weaker evidence than a high one, since a source may have labelled a text once and copied the label to its repeats:

Source	Texts labelled twice	Labelled differently
Jigsaw Unintended Bias	2,323	7.0%
East Asian Prejudice	711	12.5%
IMSyPP English YouTube corpus	490	2.9%
Davidson et al.	360	9.2%
HatEval	255	0.0%
Social Bias Frames	205	21.0%
Contextual Abuse Dataset	202	13.9%
Learning to Intervene	180	0.0%
Gab Hate Corpus	176	6.2%
Measuring Hate Speech	90	8.9%
Slur Corpus	83	0.0%
MLMA	59	50.8%
HateXplain	34	29.4%
"Call me sexist, but"	34	23.5%
Implicit Hate	21	0.0%
Fox News comments	3	0.0%
White Supremacy Forum	3	0.0%

• Slurs are not hate, and models confuse them. A word-count model trained on all sources contradicts mostly labels that call a text not hateful though it uses a slur: quoted, reclaimed, used in counter speech or merely offensive (the Slur Corpus, HateXplain, MLMA, Davidson et al.). These are not labelling errors but the hardest and most useful negatives, so do not "clean" them away; test models on them.
• Identity words trip models. The same model raised false alarms on non-hateful texts that mention Black, Jewish, white or gay people about three times as often as on other texts, since identity words co-occur with hate in training data (Dixon et al., 2018). Evaluate with subgroup metrics (Borkan et al., 2019), and balance training with the many non-hateful texts here that mention each group.
• Sources leave signatures. Sources differ in formatting (lower-casing, tokenisation, placeholders) and in how much hate they hold, so formatting can predict the label and a model can learn the source instead of the hate. In the default configuration, Cramér's V between being hateful and a text's sources is 0.48, and its platform 0.43 (0 means no association, 1 that one determines the other). Topic marks a source most (EAP's COVID hashtags, HASOC's cricket, CMSB's cookery show), and so does how it wrote links (raw, URL, @URL, <url>) and numbers (<number> in HateXplain and SBF), though these say almost nothing about the label. Hateful share by platform there:

Platform	Texts	Hateful
civil_comments	443,158	1.7%
twitter	107,290	16.1%
reddit	98,171	38.6%
gab	41,659	19.8%
youtube	8,155	28.4%
stormfront	4,016	23.3%
fox_news	1,488	28.9%
unknown	55	47.3%

• Dialect bias. Annotators have labelled African American English as offensive or hateful more often than other English (Sap et al., 2019; Davidson et al., 2019), and sources labelled that way carry the bias.
• Some posts are cut short. Platforms and sources truncated some tweets, which end in "…"; the full posts can no longer be recovered.
• A missing target means "not annotated", not "no target": several sources annotate targets only for hateful texts, and some not at all.
• English, with a trace of other languages. Of 40 texts drawn at random from those a language identifier (lingua) called non-English, 4 were (Spanish, Italian, Dutch, Tagalog); the rest were slang, hashtags and African American English, which identifiers misread as other languages (Blodgett et al., 2016). That puts other languages at about 0.1% of texts. No language filter is applied: it would remove far more English than not, and African American English above all.

Personal information

The texts are public posts released by their sources. HateAtlas replaces e-mail addresses (<email>), phone numbers (<phone>), bank account numbers (<account>) and every mention of a user (@user, and u/user on Reddit). In the default configuration the placeholders appear as @user in 43,849 texts, u/user in 355 texts, <email> in 48 texts, <phone> in 50 texts and <account> in none. Where a source names authors, they become keyed pseudonyms (HMAC-SHA-256): one author keeps one id across sources, but the id cannot be reversed by hashing guessed names. Names written in a text, usernames inside links, mentions run onto a word (you@name, which cannot be told from obfuscations such as cr@p and are kept) and the posts themselves can still identify people. To have a text removed, e-mail josh19peter96@gmail.com with its id.

Uses

HateAtlas is for research: detecting hate speech and racism, measuring bias across target groups and dialects, and studying how definitions and annotation differ between datasets. It is not for moderating or profiling real people without human review, nor for generating hateful text.

Licences and attribution

Each configuration carries the licence shown above, and each row's licences field names its sources' licences; the sources are credited under Sources. HateAtlas changes them: labels are mapped to one schema, texts are cleaned and redacted, and duplicates are merged. Four sources are released under the MIT licence, whose notices are reproduced here: Davidson et al., "Copyright (c) 2017 Tom Davidson"; MLMA, "Copyright (c) 2019 HKUST-KnowComp"; the Slur Corpus, "Copyright (c) 2020 McGill Network Dynamics Lab"; and the Fox News comments of Gao and Huang (2017), whose repository's licence reads "Copyright (c) 2010-2019 Google, Inc.". Each grants: "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED."

Citation

@misc{polaprayil2026hateatlas,
  author = {Polaprayil, Joshua Peter},
  title = {{HateAtlas}: A Target-Aware Atlas of English Hate-Speech Datasets},
  year = {2026},
  publisher = {Hugging Face},
  doi = {10.57967/hf/10899},
  howpublished = {\url{https://huggingface.co/datasets/JoshPola96/HateAtlas}}
}

Please cite the sources you use as well.

References

• Arango, A., Pérez, J. and Poblete, B. (2019). Hate Speech Detection is Not as Easy as You May Think: A Closer Look at Model Validation. SIGIR.
• Blodgett, S. L., Green, L. and O'Connor, B. (2016). Demographic Dialectal Variation in Social Media: A Case Study of African-American English. EMNLP.
• Borkan, D., Dixon, L., Sorensen, J., Thain, N. and Vasserman, L. (2019). Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification. WWW Companion.
• Davidson, T., Bhattacharya, D. and Weber, I. (2019). Racial Bias in Hate Speech and Abusive Language Detection Datasets. Workshop on Abusive Language Online.
• Dixon, L., Li, J., Sorensen, J., Thain, N. and Vasserman, L. (2018). Measuring and Mitigating Unintended Bias in Text Classification. AIES.
• Fortuna, P., Soler, J. and Wanner, L. (2020). Toxic, Hateful, Offensive or Abusive? What Are We Really Classifying? An Empirical Analysis of Hate Speech Datasets. LREC.
• Piot, P., Martín-Rodilla, P. and Parapar, J. (2024). MetaHate: A Dataset for Unifying Efforts on Hate Speech Detection. ICWSM.
• Sap, M., Card, D., Gabriel, S., Choi, Y. and Smith, N. A. (2019). The Risk of Racial Bias in Hate Speech Detection. ACL.
• Waseem, Z. and Hovy, D. (2016). Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter. NAACL Student Research Workshop.
• Wiegand, M., Ruppenhofer, J. and Kleinbauer, T. (2019). Detection of Abusive Language: the Problem of Biased Datasets. NAACL.

Joshua Peter Polaprayil · josh19peter96@gmail.com