Hate Speech Dataset Catalogue

This page catalogues datasets annotated for hate speech, online abuse, and offensive language. They may be useful for e.g. training a natural language processing system to detect this language.

The list is maintained by Leon Derczynski, Bertie Vidgen, Hannah Rose Kirk, Pica Johansson, Yi-Ling Chung, Mads Guldborg Kjeldgaard Kongsbak, Laila Sprejer, and Philine Zeinert.

We provide a list of datasets and keywords. If you would like to contribute to our catalogue or add your dataset, please see the instructions for contributing.

If you use these resources, please cite (and read!) our paper: Directions in Abusive Language Training Data: Garbage In, Garbage Out. And if you would like to find other resources for researching online hate, visit The Alan Turing Institute’s Online Hate Research Hub or read The Alan Turing Institute’s Reading List on Online Hate and Abuse Research.

If you’re looking for a good paper on online hate training datasets (beyond our paper, of course!) then have a look at ‘Resources and benchmark corpora for hate speech detection: a systematic review’ by Poletto et al. in Language Resources and Evaluation.

Please send contributions via github pull request. You can do this by visiting the source code on github and clicking the edit icon (a pencil, above the text, on the right) - more details below. There’s a commented-out markdown template at the top of this file. Accompanying data statements preferred for all corpora.

Datasets Table of Contents

Albanian
Arabic
Bengali
Chinese
Croatian
Danish
Dutch
English
Estonian
French
German
Greek
Hindi
Indonesian
Italian
Korean
Latvian
Portuguese
Polish
Russian
Slovene
Spanish
Turkish
Ukranian
Urdu

List of datasets

Albanian

Detecting Abusive Albanian

Link to publication: https://arxiv.org/abs/2107.13592
Link to data: https://doi.org/10.6084/m9.figshare.19333298.v1
Task description: Hierarchical (offensive/not; untargeted/targeted; person/group/other)
Details of task: Detect and categorise abusive language in social media data
Size of dataset: 11 874
Percentage abusive: 13.2%
Language: Albanian
Level of annotation: Posts
Platform: Instagram, Youtube
Medium: Text
Reference: Nurce, E., Keci, J., Derczynski, L., 2021. Detecting Abusive Albanian. arXiv:2107.13592
Dataset reader: 🤗 strombergnlp/shaj

Arabic

Let-Mi: An Arabic Levantine Twitter Dataset for Misogynistic Language

Link to publication: https://arxiv.org/abs/2103.10195
Link to data: https://drive.google.com/file/d/1mM2vnjsy7QfUmdVUpKqHRJjZyQobhTrW/view
Task description: Binary (misogyny/none) and Multi-class (none, discredit, derailing, dominance, stereotyping & objectiﬁcation, threat of violence, sexual harassment, damning)
Details of task: Introducing an Arabic Levantine Twitter dataset for Misogynistic language
Size of dataset: 6,603 direct tweet replies
Percentage abusive: 48.76%
Language: Arabic
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Hala Mulki and Bilal Ghanem. 2021. Let-Mi: An Arabic Levantine Twitter Dataset for Misogynistic Language. In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 154–163, Kyiv, Ukraine (Virtual). Association for Computational Linguistics

Are They our Brothers? Analysis and Detection of Religious Hate Speech in the Arabic Twittersphere

Link to publication: https://ieeexplore.ieee.org/document/8508247
Link to data: https://github.com/nuhaalbadi/Arabic_hatespeech
Task description: Binary (Hate, Not)
Details of task: Religious subcategories
Size of dataset: 6,136
Percentage abusive: 0.45
Language: Arabic
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Albadi, N., Kurdi, M. and Mishra, S., 2018. Are they Our Brothers? Analysis and Detection of Religious Hate Speech in the Arabic Twittersphere. In: International Conference on Advances in Social Networks Analysis and Mining. Barcelona, Spain: IEEE, pp.69-76.

Multilingual and Multi-Aspect Hate Speech Analysis (Arabic)

Link to publication: https://arxiv.org/abs/1908.11049
Link to data: https://github.com/HKUST-KnowComp/MLMA_hate_speech
Task description: Detailed taxonomy with cross-cutting attributes: Hostility, Directness, Target Attribute, Target Group, How annotators felt on seeing the tweet.
Details of task: Gender, Sexual orientation, Religion, Disability
Size of dataset: 3,353
Percentage abusive: 0.64
Language: Arabic
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Ousidhoum, N., Lin, Z., Zhang, H., Song, Y. and Yeung, D., 2019. Multilingual and Multi-Aspect Hate Speech Analysis. ArXiv,.

L-HSAB: A Levantine Twitter Dataset for Hate Speech and Abusive Language

Link to publication: https://www.aclweb.org/anthology/W19-3512
Link to data: https://github.com/Hala-Mulki/L-HSAB-First-Arabic-Levantine-HateSpeech-Dataset
Task description: Ternary (Hate, Abusive, Normal)
Details of task: Group-directed + Person-directed
Size of dataset: 5,846
Percentage abusive: 0.38
Language: Arabic
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Mulki, H., Haddad, H., Bechikh, C. and Alshabani, H., 2019. L-HSAB: A Levantine Twitter Dataset for Hate Speech and Abusive Language. In: Proceedings of the Third Workshop on Abusive Language Online. Florence, Italy: Association for Computational Linguistics, pp.111-118.

Link to publication: https://www.aclweb.org/anthology/W17-3008
Link to data: http://alt.qcri.org/~hmubarak/offensive/TweetClassification-Summary.xlsx
Task description: Ternary (Obscene, Offensive but not obscene, Clean)
Details of task: Incivility
Size of dataset: 1,100
Percentage abusive: 0.59
Language: Arabic
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Mubarak, H., Darwish, K. and Magdy, W., 2017. Abusive Language Detection on Arabic Social Media. In: Proceedings of the First Workshop on Abusive Language Online. Vancouver, Canada: Association for Computational Linguistics, pp.52-56.
Dataset reader: 🤗 strombergnlp/offenseval_2020

Link to publication: https://www.aclweb.org/anthology/W17-3008
Link to data: http://alt.qcri.org/~hmubarak/offensive/AJCommentsClassification-CF.xlsx
Task description: Ternary (Obscene, Offensive but not obscene, Clean)
Details of task: Incivility
Size of dataset: 32,000
Percentage abusive: 0.81
Language: Arabic
Level of annotation: Posts
Platform: AlJazeera
Medium: Text
Reference: Mubarak, H., Darwish, K. and Magdy, W., 2017. Abusive Language Detection on Arabic Social Media. In: Proceedings of the First Workshop on Abusive Language Online. Vancouver, Canada: Association for Computational Linguistics, pp.52-56.

Link to publication: https://www.sciencedirect.com/science/article/pii/S1877050918321756
Link to data: https://onedrive.live.com/?authkey=!ACDXj_ZNcZPqzy0&id=6EF6951FBF8217F9!105&cid=6EF6951FBF8217F9
Task description: Binary (Offensive, Not)
Details of task: Incivility
Size of dataset: 15,050
Percentage abusive: 0.39
Language: Arabic
Level of annotation: Posts
Platform: YouTube
Medium: Text
Reference: Alakrot, A., Murray, L. and Nikolov, N., 2018. Dataset Construction for the Detection of Anti-Social Behaviour in Online Communication in Arabic. Procedia Computer Science, 142, pp.174-181.

Bengali

Hate Speech Detection in the Bengali language: A Dataset and its Baseline Evaluation

Link to publication: https://arxiv.org/pdf/2012.09686.pdf
Link to data: https://www.kaggle.com/naurosromim/bengali-hate-speech-dataset
Task description: Binary (hateful, not)
Details of task: Several categories: sports, entertainment, crime, religion, politics, celebrity and meme
Size of dataset: 30,000
Percentage abusive: 0.33
Language: Bengali
Level of annotation: Posts
Platform: Youtube and Facebook
Medium: Text
Reference: Romim, N., Ahmed, M., Talukder, H., & Islam, M. S. (2021). Hate speech detection in the bengali language: A dataset and its baseline evaluation. In Proceedings of International Joint Conference on Advances in Computational Intelligence (pp. 457-468). Springer, Singapore.

Chinese

SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection

Link to publication: https://www.sciencedirect.com/science/article/abs/pii/S2468696421000604#fn1
Link to data: https://doi.org/10.5281/zenodo.4773875
Task description: Binary (Sexist, Non-sexist), Categories of sexism (Stereotype based on Appearance, Stereotype based on Cultural Background, MicroAggression, and Sexual Offense), Target of sexism (Individual or Generic)
Details of task: Sexism detection on social media in Chinese
Size of dataset: 8,969 comments from 1,527 weibos
Percentage abusive: 34.5%
Language: Chinese
Level of annotation: Posts
Platform: Sina Weibo
Medium: Text
Reference: Aiqi Jiang, Xiaohan Yang, Yang Liu, Arkaitz Zubiaga, SWSR: A Chinese dataset and lexicon for online sexism detection, Online Social Networks and Media, Volume 27, 2022, 100182, ISSN 2468-6964.

Croatian

CoRAL: a Context-aware Croatian Abusive Language Dataset

Link to publication: https://aclanthology.org/2022.findings-aacl.21/
Link to data: https://github.com/shekharRavi/CoRAL-dataset-Findings-of-the-ACL-AACL-IJCNLP-2022
Task description: Multi-class based on context dependency categories (CDC)
Details of task: Detectioning CDC from abusive comments
Size of dataset: 2,240
Percentage abusive: 100%
Language: Croatian
Level of annotation: Posts
Platform: Newspaper comments
Medium: Text
Reference: Ravi Shekhar, Mladen Karan and Matthew Purver (2022). CoRAL: a Context-aware Croatian Abusive Language Dataset. Findings of the ACL: AACL-IJCNLP.

Datasets of Slovene and Croatian Moderated News Comments

Link to publication: https://www.aclweb.org/anthology/W18-5116
Link to data: http://hdl.handle.net/11356/1202
Task description: Binary (Deleted, Not)
Details of task: Flagged content
Size of dataset: 17,000,000
Percentage abusive: 0.02
Language: Croatian
Level of annotation: Posts
Platform: 24sata website
Medium: Text
Reference: Ljubešić, N., Erjavec, T. and Fišer, D., 2018. Datasets of Slovene and Croatian Moderated News Comments. In: Proceedings of the 2nd Workshop on Abusive Language Online (ALW2). Brussels, Belgium: Association for Computational Linguistics, pp.124-131.

Automating News Comment Moderation with Limited Resources: Benchmarking in Croatian and Estonian

Link to publication: https://jlcl.org/content/2-allissues/1-heft1-2020/jlcl_2020-1_3.pdf
Link to data: https://www.clarin.si/repository/xmlui/handle/11356/1399
Task description: Multi-class based on Different rules
Details of task: Flagged content performmed by the real newspaper moderators
Size of dataset: 21M
Percentage abusive: 7.8%
Language: Croatian
Level of annotation: Posts
Platform: Newspaper comments
Medium: Text
Reference: Ravi Shekhar, Marko Pranjić, Senja Pollak, Andraž Pelicon, Matthew Purver (2020). Automating News Comment Moderation with Limited Resources: Benchmarking in Croatian and Estonian. Journal for Language Technology and Computational Linguistics (JLCL).

Danish

Offensive Language and Hate Speech Detection for Danish

Link to publication: http://www.derczynski.com/papers/danish_hsd.pdf
Link to data: https://figshare.com/articles/Danish_Hate_Speech_Abusive_Language_data/12220805
Task description: Branching structure of tasks: Binary (Offensive, Not), Within Offensive (Target, Not), Within Target (Individual, Group, Other)
Details of task: Group-directed + Person-directed
Size of dataset: 3,600
Percentage abusive: 0.12
Language: Danish
Level of annotation: Posts
Platform: Twitter, Reddit, newspaper comments
Medium: Text
Reference: Sigurbergsson, G. and Derczynski, L., 2019. Offensive Language and Hate Speech Detection for Danish. ArXiv.
Dataset reader: 🤗 DDSC/dkhate

BAJER: Misogyny in Danish

Link to publication: https://aclanthology.org/2021.acl-long.247/
Link to data: request here
Task description: Hierarchy of abusive content labels including subcategories of misogyny
Details of task: “Misogyny detection on social media in Danish”
Size of dataset: 27.9K comments
Percentage abusive: 7% misogynistic, 27% abusive (i.e. 20% abusive but not misogyny)
Language: Danish
Level of annotation: Social media post / comment
Platform: Twitter, Facebook, Reddit
Medium: text
Reference: Zeinert, Inie, & Derczynski, 2021. “Annotating Online Misogyny”. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL
Dataset reader: 🤗 strombergnlp/bajer_danish_misogyny

Dutch

The Dutch Abusive Language Corpus v1.0 (DALC v1.0)

Link to publication: https://aclanthology.org/2021.woah-1.6.pdf - link to the documentation and/or a data statement about the data
Link to data: https://github.com/tommasoc80/DALC
Task description: Multilayered (explicitness and target) for abusive language
Details of task: Abusive language detection in social media in Dutch
Size of dataset: 8,156 tweets
Percentage abusive: 15.06% explicitly abusive; 8.09% implicitly abusive
Language: Dutch
Level of annotation: tweets
Platform: Twitter
Medium: text
Reference: Caselli, T., Schelhaas, A., Weultjes, M., Leistra, F., van der Veen, H., Timmerman, G., and Nissim, M. 2021. “DALC: the Dutch Abusive Language Corpus”. Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021), ACL.

English

Not All Counterhate Tweets Elicit the Same Replies: A Fine-Grained Analysis

Link to publication: https://aclanthology.org/2023.starsem-1.8/
Link to data: https://github.com/albanyan/counterhate_reply
Task description: Four binary classification tasks to investigate replies to counterhate tweets (1) Binary (Agree, Not), (2) Binary (Support_Hateful-tweet, Not), (3) Binary (Attack_Author, Not), and (4) Binary (Additional_Counterhate, Not)
Details of task: Three levels of tweets are considered: a hateful tweet, a counterhate tweet (a reply to a hateful tweet), and all replies to the counterhate tweet. Indicate whether the reply to a counterhate tweet (a) agrees with the counterhate tweet, (b) supports the hateful tweet, (c) attacks the author of the counterhate tweet, and (d) adds additional counterhate
Size of dataset: 2,621 (hateful tweet, counterhate tweet, reply) triples
Percentage abusive: 100% (All main tweets are hateful tweets)
Language: English
Level of annotation: Tweets
Platform: Twitter
Medium: Text
Reference: Abdullah Albanyan, Ahmed Hassan, and Eduardo Blanco. 2023. Not All Counterhate Tweets Elicit the Same Replies: A Fine-Grained Analysis. In Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023), pages 71–88, Toronto, Canada. Association for Computational Linguistics.

Pinpointing Fine-Grained Relationships between Hateful Tweets and Replies

Link to publication: https://ojs.aaai.org/index.php/AAAI/article/view/21284
Link to data: https://github.com/albanyan/hateful-tweets-replies
Task description: Four binary classification tasks (1) Binary (Counterhate, Not), (2) Binary (Counterhate_with_Justification, Not), (3) Binary (Attack_Author, Not), and (4) Binary (Additional_Hate, Not)
Details of task: Indicate whether the reply to a hateful tweet (a) is counter hate speech, (b) provides a justification, (c) attacks the author of the tweet, and (d) adds additional hate
Size of dataset: 5,652 hateful tweets and replies
Percentage abusive: 100% (All main tweets are hateful tweets)
Language: English
Level of annotation: Tweets
Platform: Twitter
Medium: Text
Reference: Abdullah Albanyan and Eduardo Blanco. 2022. Pinpointing Fine-Grained Relationships Between Hateful Tweets and Replies. Proceedings of the AAAI Conference on Artificial Intelligence 36 (10):10418-26.

Large-Scale Hate Speech Detection with Cross-Domain Transfer

Link to publication: https://aclanthology.org/2022.lrec-1.238/
Link to data: https://github.com/avaapm/hatespeech
Task description: Three-class (Hate speech, Offensive language, None)
Details of task: Hate speech detection on social media (Twitter) including 5 target groups (gender, race, religion, politics, sports)
Size of dataset: 100k English (27593 hate, 30747 offensive, 41660 none)
Percentage abusive: 58.3%
Language: English
Level of annotation: Posts
Platform: Twitter
Medium: Text and image
Reference: Cagri Toraman, Furkan Şahinuç, Eyup Yilmaz. 2022. Large-Scale Hate Speech Detection with Cross-Domain Transfer. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 2215–2225, Marseille, France. European Language Resources Association.
#### Online-Abusive-Attacks-OAA-Dataset
Link to publication: https://ieeexplore.ieee.org/abstract/document/10160004
Link to data: https://github.com/RaneemAlharthi/Online-Abusive-Attacks-OAA-Dataset
Task description: “Binary (abusive, Notabusive)”, “Hierarchical”, “six-class (toxicity, severe toxicity, identity attack,insult, profanity, and threat)”
Details of task: “the first benchmark dataset providing a holistic view of online abusive attacks, including social media profile data and metadata for both targets and perpetrators, in addition to context. The dataset contains 2.3K Twitter accounts, 5M tweets, and 106.9K categorised conversations.”
Size of dataset: 2.3K Twitter accounts, 5M tweets, and 106.9K categorised conversations.
Percentage abusive: online abusive attacks motivated by the targets’ identities (97%), and motivated by the targets’ behavioural attacks (3%).
Language: e.g. English
Level of annotation: What is an “instance”, in this dataset? e.g. Conversation
Platform: e.g. twitter
Medium: text /metadata
Reference: @article{alharthi2023target, title={Target-Oriented Investigation of Online Abusive Attacks: A Dataset and Analysis}, author={Alharthi, Raneem and Alharthi, Rajwa and Shekhar, Ravi and Zubiaga, Arkaitz}, journal={IEEE Access}, year={2023}, publisher={IEEE} }

ConvAbuse

Link to publication: https://aclanthology.org/2021.emnlp-main.587/
Link to data: https://github.com/amandacurry/convabuse
Task description: Hierarchical: 1. Abuse binary, Abuse severity 1,0,-1,-2,-3; 2. Directedness explicit, implicit Target group, individual–system, individual–3rd party, Type general, sexist, sexual harassment, homophobic, racist, transphobic, ableist, intellectual
Details of task: Abuse detection in conversational AI
Size of dataset: 4,185
Percentage abusive: c. 20%
Language: English
Level of annotation: utterance (with conversational context)
Platform: Carbonbot on Facebook Messenger and E.L.I.Z.A. chatbots
Medium: text
Reference: Curry, A. C., Abercrombie, G., & Rieser, V. 2021. ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Detection in Conversational AI. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 7388-7403).

Measuring Hate Speech

Link to publication: https://arxiv.org/abs/2009.10277
Link to data: https://huggingface.co/datasets/ucberkeley-dlab/measuring-hate-speech
Task description: 10 ordinal labels (sentiment, (dis)respect, insult, humiliation, inferior status, violence, dehumanization, genocide, attack/defense, hate speech), which are debiased and aggregated into a continuous hate speech severity score (hate_speech_score) that includes a region for counterspeech & supportive speeech. Includes 8 target identity groups (race/ethnicity, religion, national origin/citizenship, gender, sexual orientation, age, disability, political ideology) and 42 identity subgroups.
Details of task: Hate speech measurement on social media in English
Size of dataset: 39,565 comments annotated by 7,912 annotators on 10 ordinal labels, for 1,355,560 total labels.
Percentage abusive: 25% - however this dichotomization is not in the spirit of the paper/dataset
Language: English
Level of annotation: Social media comment
Platform: Twitter, Reddit, YouTube
Medium: Text
Reference: Kennedy, C. J., Bacon, G., Sahn, A., & von Vacano, C. (2020). Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application. arXiv preprint arXiv:2009.10277.

Learning From the Worst (Dynamically generated hate speech dataset)

Link to publication: https://aclanthology.org/2021.acl-long.132/
Link to data: https://github.com/bvidgen/Dynamically-Generated-Hate-Speech-Dataset
Task description: Multi-category hate speech detection
Details of task: Hate detection with fine-grained labels for the type and target of hate. Generated over 4 rounds of human-and-model-in-the-loop adversarial data generation. Collected through Dynabench.
Size of dataset: 41,255
Percentage abusive: 54%
Language: English
Level of annotation: posts
Platform: Synthetically generated by humans to mimic real-world social media posts
Medium: text
Reference: Vidgen, B., Thurush, T., Waseem, Z., Kiela, D., 2021. Learning from the worst: dynamically generated datasets to improve online hate detection. In Proceedings of the 59th Meeting of the Association for Computational Lingusitics (pp. 1667-1682).

The ‘Call me sexist, but’ sexism dataset

Link to publication: https://ojs.aaai.org/index.php/ICWSM/article/view/18085/17888
Link to data: https://doi.org/10.7802/2251
Task description: Sexism detection based on content and phrasing
Details of task: Sexism detection on English social media data informed by survey items measuring sexist attitudes and adversarial examples
Size of dataset: 6325
Percentage abusive: 28%
Language: English
Level of annotation: tweets and survey items
Platform: Twitter, Social Psychology scales
Medium: text
Reference: Samory, M., Sen, I., Kohne, J., Flöck, F. and Wagner, C., 2021, May. Call me sexist, but…: Revisiting sexism detection using psychological scales and adversarial samples. In Intl AAAI Conf. Web and Social Media (pp. 573-584).

Hate Towards the Political Opponent: A Twitter Corpus Study of the 2020 US Elections on the Basis of Offensive Speech and Stance Detection__

Link to publication: https://aclanthology.org/2021.wassa-1.18/
Link to data: https://www.ims.uni-stuttgart.de/data/stance_hof_us2020
Task description: Hate / Offensive or neither
Details of task: Data collected to be Twitter by supporters of Trump or Biden
Size of dataset: 3,000
Percentage abusive: 12%
Language: English
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Lara Grimminger and Roman Klinger (2020): Hate Towards the Political Opponent: A Twitter Corpus Study of the 2020 US Elections on the Basis of Offensive Speech and Stance Detection. 11th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis (collocated with EACL 2021).

AbuseEval v1.0

Link to publication: http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.760.pdf
Link to data: https://github.com/tommasoc80/AbuseEval
Task description: Explicitness annotation of offensive and abusive content
Details of task: Enriched versions of the OffensEval/OLID dataset with the distinction of explicit/implicit offensive messages and the new dimension for abusive messages. Labels for offensive language: EXPLICIT, IMPLICT, NOT; Labels for abusive language: EXPLICIT, IMPLICT, NOTABU
Size of dataset: 14,100
Percentage abusive: 20.75%
Language: English
Level of annotation: tweets
Platform: Twitter
Medium: text
Reference: Caselli, T., Basile, V., Jelena, M., Inga, K., and Michael, G. 2020. “I feel offended, don’t be abusive! implicit/explicit messages in offensive and abusive language”. The 12th Language Resources and Evaluation Conference (pp. 6193-6202). European Language Resources Association.

Link to publication: https://www.aclweb.org/anthology/2020.lrec-1.765.pdf
Link to data: https://github.com/dadangewp/SWAD-Repository
Task description: Binary (abusive swear word, non-abusive swear word)
Details of task: Abusive swearing
Size of dataset: 1,511 swear words (1675 tweets)
Percentage abusive: 0.41% (word level), 0.51% (post level)
Language: English
Level of annotation: Words
Platform: Twitter
Medium: Text
Reference: Pamungkas, E. W., Basile, V., & Patti, V. (2020). Do you really want to hurt me? predicting abusive swearing in social media. In The 12th Language Resources and Evaluation Conference (pp. 6237-6246). European Language Resources Association.

Multimodal Meme Dataset (MultiOFF) for Identifying Offensive Content in Image and Text

Link to publication: https://www.aclweb.org/anthology/2020.trac-1.6.pdf
Link to data: https://github.com/bharathichezhiyan/Multimodal-Meme-Classification-Identifying-Offensive-Content-in-Image-and-Text
Task description: Binary (offensive, non-offensive)
Details of task: Hate per se (related to 2016 U.S. presidential election)
Size of dataset: 743
Percentage abusive: 0.41%
Language: English
Level of annotation: Posts
Platform: Kaggle, Reddit, Facebook, Twitter and Instagram
Medium: Text and Images/memes
Reference: Suryawanshi, S., Chakravarthi, B. R., Arcan, M., & Buitelaar, P. (2020, May). Multimodal meme dataset (MultiOFF) for identifying offensive content in image and text. In Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (pp. 32-41).

Hatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-based Hate

Link to publication: https://arxiv.org/abs/2108.05921
Link to data: https://github.com/HannahKirk/Hatemoji
Task description: Branching structure of tasks: Binary (Hate, Not Hate), Within Hate (Type, Target)
Details of task: Hate speech detection for text statements including emoji, consisting of a checklist-based test suite (HatemojiCheck) and an adversarially-generated dataset (HatemojiBuild)
Size of dataset: HatemojiCheck = 3,930; HatemojiBuild = 5,912.
Percentage abusive: HatemojiCheck = 69%, HatemojiBuild = 50%
Language: English
Level of annotation: Post
Platform: Synthetically-Generated
Medium: Text with emoji
Reference: Kirk, H. R., Vidgen, B., Röttger, P., Thrush, T., & Hale, S. A. 2021. Hatemoji: A test suite and adversarially-generated dataset for benchmarking and detecting emoji-based hate. arXiv preprint arXiv:2108.05921.

HateCheck: Functional Tests for Hate Speech Detection Models

Link to publication: https://arxiv.org/pdf/2012.15606.pdf
Link to data: https://github.com/paul-rottger/hatecheck-data
Task description: Binary (Hate, Not Hate), 7 Targets Within Hate (Women, Trans people, Black people, Gay people, Disabled people, Muslims, Immigrants)
Details of task: A checklist of functional tests to evaluate hate speech detection models.
Size of dataset: 3,728
Percentage abusive: 68%
Language: English
Level of annotation: Post
Platform: Synthetically-Generated
Medium: Text
Reference: Röttger, P., Vidgen, B., Nguyen, D., Waseem, Z., Margetts, H. and Pierrehumbert, J., 2020. Hatecheck: Functional tests for hate speech detection models. arXiv preprint arXiv:2012.15606.

Semeval-2021 Task 5: Toxic Spans Detection

Link to publication: https://aclanthology.org/2021.semeval-1.6.pdf
Link to data: https://github.com/ipavlopoulos/toxic_spans
Task description: Binary toxic spans (toxic, non-toxic) & reading comprehension
Details of task: Predict the spans of toxic posts that were responsible for the toxic label of the posts.
Size of dataset: 10,629
Percentage abusive: 0.56
Language: English
Level of annotation: Posts
Platform: Civil Comments
Medium: Text
Reference: Pavlopoulos, J., Sorensen, J., Laugier, L., & Androutsopoulos, I. (2021, August). Semeval-2021 task 5: Toxic spans detection. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021) (pp. 59-69).

ToxiSpanSE: An Explainable Toxicity Detection in Code Review Comments

Link to publication: https://arxiv.org/abs/2307.03386
Link to data and tool: (https://github.com/WSU-SEAL/ToxiSpanSE)
Task description: Binary toxic spans (Toxic, Non-toxic)
Details of task: Toxicity, Context
Size of dataset: 19,651
Percentage of toxic in span level: 13.85
Language: English
Level of annotation: Code Review Comments
Platform: Open Source Software
Medium: Text
Reference: Sarker, Jaydeb, Sultana, Sayma, Wilson, Steven R., and Amiangshu Bosu. “ToxiSpanSE: An Explainable Toxicity Detection in Code Review Comments” The 17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), 2023.

Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech

Link to publication: https://aclanthology.org/2021.acl-long.250.pdf
Link to data: https://github.com/marcoguerini/CONAN
Task description: Binary (hateful, not)
Details of task: race, religion, country of origin, sexual orientation, disability, gender
Size of dataset: 5,003
Percentage abusive: 1
Language: English
Level of annotation: Posts
Platform: Semi-synthetic text
Medium: Text
Reference: Margherita Fanton, Helena Bonaldi, Serra Sinem Tekiroğlu, Marco Guerini Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics: Long Papers.

HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection

Link to publication: https://arxiv.org/abs/2012.10289
Link to data: https://github.com/punyajoy/HateXplain
Task description: Level of hate (hate, offensive or normal), on target groups (race, religion, gender, sexual orientation, miscellaneous), and rationales
Details of task: Hate per se
Size of dataset: 20,148
Percentage abusive: 0.57
Language: English
Level of annotation: Words, phrases, posts
Platform: Twitter and Gab
Medium: Text
Reference: Mathew, B., Saha, P., Yimam, S. M., Biemann, C., Goyal, P., & Mukherjee, A. (2021, May). HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 17, pp. 14867-14875).

ALONE: A Dataset for Toxic Behavior among Adolescents on Twitter

Link to publication: https://arxiv.org/pdf/2008.06465.pdf
Link to data: Data made available upon request, please email Ugur Kursuncu ugur@gsu.edu and thilini@sc.edu thilini@sc.edu.
Task description: Binary (Toxic, Non-Toxic)
Details of task: Annotates interactions (Tweets and their replies), and assigns keywords describing use of emojis, URL content and images.
Size of dataset: 688
Percentage abusive: 0.17
Language: English
Level of annotation: Post
Platform: Twitter
Medium: Multimodal (text, images, emojis, metadata)
Reference: Wijesiriwardene, T., Inan, H., Kursuncu, U., Gaur, M., Shalin, V., Thirunarayan, K., Sheth, A. and Arpinar, I., 2020, Arxiv.

Towards a Comprehensive Taxonomy and Large-Scale Annotated Corpus for Online Slur Usage

Link to publication: https://www.aclweb.org/anthology/2020.alw-1.17.pdf
Link to data: https://github.com/networkdynamics/slur-corpus
Task description: 4 primary categories (derogatory, appropriate, non-derogatory/non-appropriate, homonyms, noise)
Details of task: Hate per se
Size of dataset: 39,811
Percentage abusive: 0.52
Language: English
Level of annotation: Posts
Platform: Reddit
Medium: Text
Reference: Kurrek, J., Saleem, H. M., & Ruths, D. (2020, November). Towards a comprehensive taxonomy and large-scale annotated corpus for online slur usage. In Proceedings of the Fourth Workshop on Online Abuse and Harms (pp. 138-149).

Multimodal Meme Dataset (MultiOFF) for Identifying Offensive Content in Image and Text

Link to publication: https://www.aclweb.org/anthology/2020.trac-1.6.pdf
Link to data: https://www.aclweb.org/anthology/2020.trac-1.6.pdf
Task description: Binary (offensive, non-offensive)
Details of task: Hate per se (related to 2016 U.S. presidential election)
Size of dataset: 743
Percentage abusive: 0.41
Language: English
Level of annotation: Posts
Platform: Kaggle, Reddit, Facebook, Twitter and Instagram
Medium: Text and Images/memes
Reference: Suryawanshi, S., Chakravarthi, B. R., Arcan, M., & Buitelaar, P. (2020, May). Multimodal meme dataset (MultiOFF) for identifying offensive content in image and text. In Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (pp. 32-41).

Link to publication: https://aclanthology.org/N19-1144.pdf
Link to data: https://scholar.harvard.edu/malmasi/olid
Task description: Branching structure of tasks. A: offensive / not, B: targeted insult / untargeted, C: individual, group, other.
Details of task: Hate per se
Size of dataset: 14,100
Percentage abusive: 0.33
Language: English
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Zampieri, M., Malmasi, S., Nakov, P., Rosenthal, S., Farra, N., & Kumar, R. (2019, June). Predicting the Type and Target of Offensive Posts in Social Media. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (pp. 1415-1420).

Nuanced metrics for measuring unintended bias with real data for text classification

Link to publication: https://arxiv.org/pdf/1903.04561.pdf
Link to data: https://www.tensorflow.org/datasets/catalog/civil_comments
Task description: Toxicity (severe, obscene, threat, insult, identity attack, sexual explicit), and several identity attributes (e.g., gender, religion and race)
Details of task: Hate per se
Size of dataset: 1,804,875
Percentage abusive: 0.8
Language: English
Level of annotation: Comments/posts
Platform: Civil Comments
Medium: Text
Reference: Borkan, D., Dixon, L., Sorensen, J., Thain, N., & Vasserman, L. (2019, May). Nuanced metrics for measuring unintended bias with real data for text classification. In Companion proceedings of the 2019 world wide web conference (pp. 491-500).

Introducing CAD: the Contextual Abuse Dataset

Link to publication: https://aclanthology.org/2021.naacl-main.182.pdf
Link to data: https://zenodo.org/record/4881008#.Ye6OwhP7R6o
Task description: Contextually abusive language, person-directed + group-directed
Details of task: Primary categories (secondary categories): Abusive + Identity-directed (derogation/animosity/threatening/glorification/dehumanization), Abusive + Person-directed (derogation/animosity/threatening/glorification/dehumanization), Abusive + Affiliation directed (abuse to them/abuse about them), Counter Speech (against identity-directed abuse/against affiliation-directed abuse/against person-directed abuse), Non-hateful Slurs and Neutral.
Size of dataset: 25,000
Percentage abusive: Affiliation-directed, 6%; Identity-directed, 13%; Person-directed, 5%
Language: English
Level of annotation: Conversation thread
Platform: Reddit
Medium: Text
Reference: Vidgen, B., Nguyen, D., Margetts, H., Rossini, P., and Troble, R., Introducing CAD: the Contextual Abuse Dataset, 2021, In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp.2289–2303

Automated Hate Speech Detection and the Problem of Offensive Language

Link to publication: [https://ojs.aaai.org/index.php/ICWSM/article/view/14955)
Link to data: https://github.com/t-davidson/hate-speech-and-offensive-language
Task description: Hierarchy (Hate, Offensive, Neither)
Details of task: Hate per se
Size of dataset: 24,802
Percentage abusive: 0.06
Language: English
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Davidson, T., Warmsley, D., Macy, M., & Weber, I. 2017. Automated Hate Speech Detection and the Problem of Offensive Language. Proceedings of the International AAAI Conference on Web and Social Media, 11(1), 512-515.

Hate Speech Dataset from a White Supremacy Forum

Link to publication: https://www.aclweb.org/anthology/W18-5102.pdf
Link to data: https://github.com/Vicomtech/hate-speech-dataset
Task description: Ternary (Hate, Relation, Not)
Details of task: Hate per se
Size of dataset: 9,916
Percentage abusive: 0.11
Language: English
Level of annotation: Sentence - with context of the converstaional thread taken into account
Platform: Stormfront
Medium: Text
Reference: de Gibert, O., Perez, N., García-Pablos, A., and Cuadros, M., 2018. Hate Speech Dataset from a White Supremacy Forum. In: Proceedings of the 2nd Workshop on Abusive Language Online (ALW2). Brussels, Belgium: Association for Computational Linguistics, pp.11-20.

Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter

Link to publication: https://www.aclweb.org/anthology/N16-2013
Link to data: https://github.com/ZeerakW/hatespeech
Task description: 3-topic (Sexist, Racist, Not)
Details of task: Racism, Sexism
Size of dataset: 16,914
Percentage abusive: 0.32
Language: English
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Waseem, Z. and Horvy, D., 2016. Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter. In: Proceedings of the NAACL Student Research Workshop. San Diego, California: Association for Computational Linguistics, pp.88-93.

Detecting Online Hate Speech Using Context Aware Models

Link to publication: https://arxiv.org/pdf/1710.07395.pdf
Link to data: https://github.com/sjtuprog/fox-news-comments
Task description: Binary (Hate / not)
Details of task: Hate per se
Size of dataset: 1528
Percentage abusive: 0.28
Language: English
Level of annotation: Posts
Platform: Fox News
Medium: Text
Reference: Gao, L. and Huang, R., 2018. Detecting Online Hate Speech Using Context Aware Models. ArXiv,.

The Gab Hate Corpus: A collection of 27k posts annotated for hate speech

Link to publication: https://psyarxiv.com/hqjxn/
Link to data: https://osf.io/edua3/
Task description: Binary (Hate vs. Offensive/Vulgarity), Binary (Assault on human Dignity/Call for Violence – sub task on message delivery, binary: explicit/implicit), Multinomial classification: Identity based hate (race/ethnicity, nationality/regionalism/xenophobia, gender, religion/belief system, sexual orientation, ideology, political identification/party, mental/physical health)
Details of task: Group-directed + Person-directed
Size of dataset: 27,665
Percentage abusive: 0.09 Hate, 0.06 Offensive/Vulgar
Language: English
Level of annotation: Post
Platform: Gab
Medium: Text
Reference: Kennedy, B., Araria, M., Mostafazadeh Davani, A., Yeh, L., Omrani, A., Kim, Y., Koombs, K., Havaldar, S., Portillo-Wightman, G., Gonzalez, E., Hoover, J., Azatain, A., Hussain, A., Lara, A., Olmos, G., Omary, A., Park, C., Wang, C., Wang, X., Zhang, Y. and Dehghani, M., 2018, The Gab Hate Corpus: A collection of 27k posts annotated for hate speech. PsyArXiv.

Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech Detection on Twitter

Link to publication: https://pdfs.semanticscholar.org/3eeb/b7907a9b94f8d65f969f63b76ff5f643f6d3.pdf
Link to data: https://github.com/ZeerakW/hatespeech
Task description: Multi-topic (Sexist, Racist, Neither, Both)
Details of task: Racism, Sexism
Size of dataset: 4,033
Percentage abusive: 0.16
Language: English
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Waseem, Z., 2016. Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech Detection on Twitter. In: Proceedings of 2016 EMNLP Workshop on Natural Language Processing and Computational Social Science. Copenhagen, Denmark: Association for Computational Linguistics, pp.138-142.

When Does a Compliment Become Sexist? Analysis and Classification of Ambivalent Sexism Using Twitter Data

Link to publication: https://pdfs.semanticscholar.org/225f/f8a6a562bbb64b22cebfcd3288c6b930d1ef.pdf
Link to data: https://github.com/AkshitaJha/NLP_CSS_2017
Task description: Hierarchy of Sexism (Benevolent sexism, Hostile sexism, None)
Details of task: Sexism
Size of dataset: 712
Percentage abusive: 1
Language: English
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Jha, A. and Mamidi, R., 2017. When does a Compliment become Sexist? Analysis and Classification of Ambivalent Sexism using Twitter Data. In: Proceedings of the Second Workshop on Natural Language Processing and Computational Social Science. Vancouver, Canada: Association for Computational Linguistics, pp.7-16.

Overview of the Task on Automatic Misogyny Identification at IberEval 2018 (English)

Link to publication: http://ceur-ws.org/Vol-2150/overview-AMI.pdf
Link to data: https://amiibereval2018.wordpress.com/im nt-dates/data/
Task description: Binary (misogyny / not), 5 categories (stereotype, dominance, derailing, sexual harassment, discredit), target of misogyny (active or passive)
Details of task: Sexism
Size of dataset: 3,977
Percentage abusive: 0.47
Language: English
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Fersini, E., Rosso, P. and Anzovino, M., 2018. Overview of the Task on Automatic Misogyny Identification at IberEval 2018. In: Proceedings of the Third Workshop on Evaluation of Human Language Technologies for Iberian Languages (IberEval 2018).

CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech (English)

Link to publication: https://www.aclweb.org/anthology/P19-1271.pdf
Link to data: https://github.com/marcoguerini/CONAN
Task description: Binary (Islamophobic / not), multi-topic (Culture, Economics, Crimes, Rapism, Terrorism, Women Oppression, History, Other/generic)
Details of task: Islamophobia
Size of dataset: 1,288
Percentage abusive: 1
Language: English
Level of annotation: Posts
Platform: Synthetic / Facebook
Medium: Text
Reference: Chung, Y., Kuzmenko, E., Tekiroglu, S. and Guerini, M., 2019. CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence, Italy: Association for Computational Linguistics, pp.2819-2829.

Characterizing and Detecting Hateful Users on Twitter

Link to publication: https://arxiv.org/pdf/1803.08977.pdf
Link to data: https://github.com/manoelhortaribeiro/HatefulUsersTwitter
Task description: Binary (hateful/not)
Details of task: Hate per se
Size of dataset: 4,972
Percentage abusive: 0.11
Language: English
Level of annotation: Users
Platform: Twitter
Medium: Text
Reference: Ribeiro, M., Calais, P., Santos, Y., Almeida, V. and Meira, W., 2018. Characterizing and Detecting Hateful Users on Twitter. ArXiv,.

A Benchmark Dataset for Learning to Intervene in Online Hate Speech (Gab)

Link to publication: https://arxiv.org/abs/1909.04251
Link to data: https://github.com/jing-qian/A-Benchmark-Dataset-for-Learning-to-Intervene-in-Online-Hate-Speech
Task description: Binary (hateful/not)
Details of task: Hate per se
Size of dataset: 33,776
Percentage abusive: 0.43
Language: English
Level of annotation: Posts (in the context of a conversation)
Platform: Gab
Medium: Text
Reference: Qian, J., Bethke, A., Belding, E. and Yang Wang, W., 2019. A Benchmark Dataset for Learning to Intervene in Online Hate Speech. ArXiv,.

A Benchmark Dataset for Learning to Intervene in Online Hate Speech (Reddit)

Link to publication: https://arxiv.org/abs/1909.04251
Link to data: https://github.com/jing-qian/A-Benchmark-Dataset-for-Learning-to-Intervene-in-Online-Hate-Speech
Task description: Binary (hateful/not)
Details of task: Hate per se
Size of dataset: 22,324
Percentage abusive: 0.24
Language: English
Level of annotation: Posts (with context of the converstaional thread taken into account)
Platform: Reddit
Medium: Text
Reference: Qian, J., Bethke, A., Belding, E. and Yang Wang, W., 2019. A Benchmark Dataset for Learning to Intervene in Online Hate Speech. ArXiv,.

Multilingual and Multi-Aspect Hate Speech Analysis (English)

Link to publication: https://arxiv.org/abs/1908.11049
Link to data: https://github.com/HKUST-KnowComp/MLMA_hate_speech
Task description: Detailed taxonomy with cross-cutting attributes: Hostility, Directness, Target attribute and Target group.
Details of task: Gender, Sexual orientation, Religion, Disability
Size of dataset: 5,647
Percentage abusive: 0.76
Language: English
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Ousidhoum, N., Lin, Z., Zhang, H., Song, Y. and Yeung, D., 2019. Multilingual and Multi-Aspect Hate Speech Analysis. ArXiv,.

Exploring Hate Speech Detection in Multimodal Publications

Link to publication: https://arxiv.org/pdf/1910.03814.pdf
Link to data: https://drive.google.com/file/d/1S9mMhZFkntNnYdO-1dZXwF_8XIiFcmlF/view
Task description: Multimodal Hate Speech Detection, including six primary categories (No attacks to any community, Racist, Sexist, Homophobic, Religion based attack, Attack to other community)
Details of task: Racism, Sexism, Homophobia, Religion-based attack
Size of dataset: 149,823
Percentage abusive: 0.25
Language: English
Level of annotation: Posts
Platform: Twitter
Medium: Text and Images/Memes
Reference: Gomez, R., Gibert, J., Gomez, L. and Karatzas, D., 2020. In Proceedings of the IEEE/CVF winter conference on applications of computer vision (pp. 1470-1478).

Link to publication: https://arxiv.org/pdf/1902.09666.pdf
Link to data: http://competitions.codalab.org/ competitions/20011
Task description: Branching structure of tasks: Binary (Offensive, Not), Within Offensive (Target, Not), Within Target (Individual, Group, Other)
Details of task: Group-directed + Person-directed
Size of dataset: 14,100
Percentage abusive: 0.33
Language: English
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Zampieri, M., Malmasi, S., Nakov, P., Rosenthal, S., Farra, N. and Kumar, R., 2019. SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval). ArXiv,.

hatEval, SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter (English)

Link to publication: https://www.aclweb.org/anthology/S19-2007
Link to data: http://competitions.codalab.org/competitions/19935
Task description: Branching structure of tasks: Binary (Hate, Not), Within Hate (Group, Individual), Within Hate (Agressive, Not)
Details of task: Group-directed + Person-directed
Size of dataset: 13,000
Percentage abusive: 0.4
Language: English
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Basile, V., Bosco, C., Fersini, E., Nozza, D., Patti, V., Pardo, F., Rosso, P. and Sanguinetti, M., 2019. SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter. In: Proceedings of the 13th International Workshop on Semantic Evaluation. Minneapolis, Minnesota: Association for Computational Linguistics, pp.54-63.

Peer to Peer Hate: Hate Speech Instigators and Their Targets

Link to publication: https://aaai.org/ocs/index.php/ICWSM/ICWSM18/paper/view/17905/16996
Link to data: https://github.com/mayelsherif/hate_speech_icwsm18
Task description: Binary (Hate/Not), only for tweets which have both a Hate Instigator and Hate Target
Details of task: Hate per se
Size of dataset: 27,330
Percentage abusive: 0.98
Language: English
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: ElSherief, M., Nilizadeh, S., Nguyen, D., Vigna, G. and Belding, E., 2018. Peer to Peer Hate: Hate Speech Instigators and Their Targets. In: Proceedings of the Twelfth International AAAI Conference on Web and Social Media (ICWSM 2018). Santa Barbara, California: University of California, pp.52-61.

Overview of the HASOC track at FIRE 2019: Hate Speech and Offensive Content Identification in Indo-European Languages

Link to publication: https://dl.acm.org/doi/pdf/10.1145/3368567.3368584?download=true
Link to data: https://hasocfire.github.io/hasoc/2019/dataset.html
Task description: Branching structure of tasks. A: Hate / Offensive or Neither, B: Hatespeech, Offensive, or Profane, C: Targeted or Untargeted
Details of task: Group-directed + Person-directed
Size of dataset: 7,005
Percentage abusive: 0.36
Language: English
Level of annotation: Posts
Platform: Twitter and Facebook
Medium: Text
Reference: Mandl, T., Modha, S., Majumder, P., Patel, D., Dave, M., Mandlia, C. and Patel, A., 2019. Overview of the HASOC track at FIRE 2019. In: Proceedings of the 11th Forum for Information Retrieval Evaluation,.

Link to publication: https://www.aclweb.org/anthology/2020.alw-1.19.pdf
Link to data: https://zenodo.org/record/3816667
Task description: Task 1: Thematic annotation (East Asia/Covid-19) Task 2: Primary category annotation: 1) Hostility against an East Asian (EA) entity 2) Criticism of an East Asian entity 3) Counter speech 5) Discussion of East Asian prejudice 5) Non-related. Task 3: Secondary category annotation (if (1) or (2) - identifying what East Asian entity was targeted + if (1) interpersonal abuse/threatening language/dehumanization).
Details of task: Detecting East Asian prejudice
Size of dataset: 20,000
Percentage abusive: 27% (Hostility, 19.5%; Criticism, 7.2%)
Language: English
Level of annotation: Post
Platform: Twitter
Medium: Text
Reference: Vidgen, B., Botelho, A., Broniatowski, D., Guest, E., Hall, M., Margetts, H., Tromble, R., Waseem, Z. and Hale, S., Detecting East Asian Prejudice on Social media, 2020, In: Proceedings of the Fourth Workshop on Online Abuse and Harms, pp.162–172

Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior

Link to publication: https://arxiv.org/pdf/1802.00393.pdf
Link to data: https://dataverse.mpi-sws.org/dataset.xhtml?persistentId=doi:10.5072/FK2/ZDTEMN
Task description: Multi-thematic (Abusive, Hateful, Normal, Spam)
Details of task: Hate per se
Size of dataset: 80,000
Percentage abusive: 0.18
Language: English
Level of annotation: Posts
Platform: Twitter
Medium: Text
Annotation process: Very detailed information is given: multiple rounds, using a smaller 300 tweet dataset for testing the schema. For the final 80k, 5 judgements per tweet. CrowdFlower
Annotation agreement: 55.9% = 4/5, 36.6% = 3/5, 7.5% = 2/5
Reference: Founta, A., Djouvas, C., Chatzakou, D., Leontiadis, I., Blackburn, J., Stringhini, G., Vakali, A., Sirivianos, M. and Kourtellis, N., 2018. Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior. ArXiv,.

A Large Labeled Corpus for Online Harassment Research

Link to publication: http://www.cs.umd.edu/~golbeck/papers/trolling.pdf
Link to data: jgolbeck@umd.edu
Task description: Binary (Harassment, Not)
Details of task: Person-directed
Size of dataset: 35,000
Percentage abusive: 0.16
Language: English
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Golbeck, J., Ashktorab, Z., Banjo, R., Berlinger, A., Bhagwan, S., Buntain, C., Cheakalos, P., Geller, A., Gergory, Q., Gnanasekaran, R., Gnanasekaran, R., Hoffman, K., Hottle, J., Jienjitlert, V., Khare, S., Lau, R., Martindale, M., Naik, S., Nixon, H., Ramachandran, P., Rogers, K., Rogers, L., Sarin, M., Shahane, G., Thanki, J., Vengataraman, P., Wan, Z. and Wu, D., 2017. A Large Labeled Corpus for Online Harassment Research. In: Proceedings of the 2017 ACM on Web Science Conference. New York: Association for Computing Machinery, pp.229-233.

Ex Machina: Personal Attacks Seen at Scale, Personal attacks

Link to publication: https://arxiv.org/pdf/1610.08914
Link to data: https://github.com/ewulczyn/wiki-detox
Task description: Binary (Personal attack, Not)
Details of task: Person-directed
Size of dataset: 115,737
Percentage abusive: 0.12
Language: English
Level of annotation: Posts
Platform: Wikipedia
Medium: Text
Reference: Wulczyn, E., Thain, N. and Dixon, L., 2017. Ex Machina: Personal Attacks Seen at Scale. ArXiv,.

Ex Machina: Personal Attacks Seen at Scale, Toxicity

Link to publication: https://arxiv.org/pdf/1610.08914
Link to data: https://github.com/ewulczyn/wiki-detox
Task description: Toxicity/healthiness judgement (-2 == very toxic, 0 == neutral, 2 == very healthy)
Details of task: Person-directed
Size of dataset: 100,000
Percentage abusive: NA
Language: English
Level of annotation: Posts
Platform: Wikipedia
Medium: Text
Reference: Wulczyn, E., Thain, N. and Dixon, L., 2017. Ex Machina: Personal Attacks Seen at Scale. ArXiv,.

Detecting cyberbullying in online communities (World of Warcraft)

Link to publication: http://aisel.aisnet.org/ecis2016_rp/61/
Link to data: http://ub-web.de/research/
Task description: Binary (Harassment, Not)
Details of task: Person-directed
Size of dataset: 16,975
Percentage abusive: 0.01
Language: English
Level of annotation: Posts
Platform: World of Warcraft
Medium: Text
Reference: Bretschneider, U. and Peters, R., 2016. Detecting Cyberbullying in Online Communities. Research Papers, 61.

Detecting cyberbullying in online communities (League of Legends)

Link to publication: http://aisel.aisnet.org/ecis2016_rp/61/
Link to data: http://ub-web.de/research/
Task description: Binary (Harassment, Not)
Details of task: Person-directed
Size of dataset: 17,354
Percentage abusive: 0.01
Language: English
Level of annotation: Posts
Platform: League of Legends
Medium: Text
Reference: Bretschneider, U. and Peters, R., 2016. Detecting Cyberbullying in Online Communities. Research Papers, 61.

A Quality Type-aware Annotated Corpus and Lexicon for Harassment Research

Link to publication: https://arxiv.org/pdf/1802.09416.pdf
Link to data: https://github.com/Mrezvan94/Harassment-Corpus
Task description: Multi-topic harassment detection
Details of task: Racism, Sexism, Appearance-related, Intellectual, Political
Size of dataset: 24,189
Percentage abusive: 0.13
Language: English
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Rezvan, M., Shekarpour, S., Balasuriya, L., Thirunarayan, K., Shalin, V. and Sheth, A., 2018. A Quality Type-aware Annotated Corpus and Lexicon for Harassment Research. ArXiv,.

Ex Machina: Personal Attacks Seen at Scale, Aggression and Friendliness

Link to publication: https://arxiv.org/pdf/1610.08914
Link to data: https://github.com/ewulczyn/wiki-detox
Task description: Aggression/friendliness judgement on a 5 point scale. (-2 == very aggressive, 0 == neutral, 3 == very friendly).
Details of task: Person-Directed + Group-Directed
Size of dataset: 160,000
Percentage abusive: NA
Language: English
Level of annotation: Posts
Platform: Wikipedia
Medium: Text
Reference: Wulczyn, E., Thain, N. and Dixon, L., 2017. Ex Machina: Personal Attacks Seen at Scale. ArXiv,.

Are Chess Discussions Racist? An Adversarial Hate Speech Data Set

Link to publication: https://arxiv.org/pdf/2011.10280.pdf
Link to data: https://www.cs.cmu.edu/~akhudabu/Chess.html
Task description: Not Labeled
Details of task: Racism, Misclassification
Size of dataset: 1,000
Percentage abusive: 0.0
Language: English
Level of annotation: Posts
Platform: Youtube
Medium: Text
Reference: Rupak Sarkar and Ashiqur R. KhudaBukhsh, Nov. 2020. Are Chess Discussions Racist? An Adversarial Hate Speech Data Set. In: The Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI} 2021

ETHOS: an Online Hate Speech Detection Dataset (Binary)

Link to publication: https://arxiv.org/pdf/2006.08328.pdf
Link to data: https://github.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset
Task description: Binary (Hate, Not)
Details of task: Gender, Race, National Origin, Disability, Religion, Sexual Orientation
Size of dataset: 998
Percentage abusive: 0.43
Language: English
Level of annotation: Posts
Platform: Youtube, Reddit
Medium: Text
Reference: Mollas, I., Chrysopoulou, Z., Karlos, S., and Tsoumakas, G., 2021. ETHOS: an Online Hate Speech Detection Dataset. Complex & Intelligent Systems, Jan. 2022

ETHOS: an Online Hate Speech Detection Dataset (Multi label)

Link to publication: https://arxiv.org/pdf/2006.08328.pdf
Link to data: https://github.com/intelligence-csd-auth-gr/Ethos-Hate-Speech-Dataset
Task description: 8 Categories (Violence, Directed/Undirected, Gender, Race, National Origin, Disability, Sexual Orientation, Religion)
Details of task: Gender, Race, National Origin, Disability, Religion, Sexual Orientation
Size of dataset: 433
Percentage abusive: 0.33
Language: English
Level of annotation: Posts
Platform: Youtube, Reddit
Medium: Text
Reference: Mollas, I., Chrysopoulou, Z., Karlos, S., and Tsoumakas, G., 2021. ETHOS: an Online Hate Speech Detection Dataset. Complex & Intelligent Systems, Jan. 2022

Twitter Sentiment Analysis

Link to publication: NA
Link to data: https://www.kaggle.com/arkhoshghalb/twitter-sentiment-analysis-hatred-speech
Task description: Binary (Hate, Not)
Details of task: Racism, Sexism
Size of dataset: 31,961
Percentage abusive: 0.07
Language: English
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Ali Toosi, Jan 2019. Twitter Sentiment Analysis

Toxicity Detection in Software Engineering: Automated Identification of Toxic Code Reviews Using ToxiCR

Link to publication: https://dl.acm.org/doi/abs/10.1145/3583562
Link to data: https://github.com/WSU-SEAL/ToxiCR
Task description: Binary (Toxic, Non-toxic)
Details of task: Toxicity, Context
Size of dataset: 19,651
Percentage of toxic: 19
Language: English
Level of annotation: Code Review Comments
Platform: Open Source Software
Medium: Text
Reference: Sarker, Jaydeb, Asif Kamal Turzo, Ming Dong, and Amiangshu Bosu. “Automated Identification of Toxic Code Reviews Using ToxiCR.” ACM Transactions on Software Engineering and Methodology (2023).

Toxicity Detection: Does Context Really Matter? CAT-LARGE (No Context)

Link to publication: https://arxiv.org/pdf/2006.00998.pdf
Link to data: https://github.com/ipavlopoulos/context_toxicity
Task description: Binary (Toxic, Non-toxic)
Details of task: Toxicity, Context
Size of dataset: 10,000
Percentage abusive: 0.006
Language: English
Level of annotation: Post
Platform: Wikipedia Talk Pages
Medium: Text
Reference: Pavlopoulos, J., Sorensen, J., Dixon, L., Thain, N., & Androutsopoulos, I. (2020). Toxicity Detection: Does Context Really Matter? ArXiv:2006.00998 [Cs].

Toxicity Detection: Does Context Really Matter? CAT-LARGE (With Context)

Link to publication: https://arxiv.org/pdf/2006.00998.pdf
Link to data: https://github.com/ipavlopoulos/context_toxicity
Task description: Binary (Toxic, Non-toxic)
Details of task: Toxicity, Context
Size of dataset: 10,000
Percentage abusive: 0.02
Language: English
Level of annotation: Post
Platform: Wikipedia Talk Pages
Medium: Text
Reference: Pavlopoulos, J., Sorensen, J., Dixon, L., Thain, N., & Androutsopoulos, I. (2020). Toxicity Detection: Does Context Really Matter? ArXiv:2006.00998 [Cs].

Anatomy of Online Hate: Developing a Taxonomy and Machine Learning Models for Identifying and Classifying Hate in Online News Media

Link to publication: https://www.aaai.org/ocs/index.php/ICWSM/ICWSM18/paper/viewFile/17885/17024
Link to data: https://www.dropbox.com/s/21wtzy9arc5skr8/ICWSM18%20-%20SALMINEN%20ET%20AL.xlsx?dl=0
Task description: Binary (Hate, Not), Multinomial classification (21 categories divided into ‘hateful language’, ‘hate targets’ and ‘hate sub-targets’)
Details of task: Group-directed + Person-directed
Size of dataset: 5,143
Percentage abusive: 82%
Language: English
Level of annotation: Comment
Platform: YouTube and Facebook
Medium: Text
Reference: Salminen, J., Almerekhi, H., Milenković, M., Jung, S., An, J., Kwak, H. and Jansen, B., 2018, Anatomy of Online Hate: Developing a Taxonomy and Machine Learning Models for Identifying and Classifying Hate in Online News Media, In: Proceedings of the Twelfth International AAAI Conference on Web and Social Media (ICWSM 2018), pp.330-339

Estonian

Automating News Comment Moderation with Limited Resources: Benchmarking in Croatian and Estonian

Link to publication: https://jlcl.org/content/2-allissues/1-heft1-2020/jlcl_2020-1_3.pdf
Link to data: http://hdl.handle.net/11356/1401
Task description: Binary (Deleted, Not)
Details of task: Flagged content performmed by the real newspaper moderators
Size of dataset: 31.5M
Percentage abusive: 12.5%
Language: Estonian (some in Russian also)
Level of annotation: Posts
Platform: Newspaper comments
Platform: Eesti Ekspress (www.ekspress.ee) website
Medium: Text
Reference: Ravi Shekhar, Marko Pranjić, Senja Pollak, Andraž Pelicon, Matthew Purver (2020). Automating News Comment Moderation with Limited Resources: Benchmarking in Croatian and Estonian. Journal for Language Technology and Computational Linguistics (JLCL).

HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection

Link to publication: https://arxiv.org/pdf/2012.10289.pdf
Link to data: https://github.com/punyajoy/HateXplain
Task description: Binary (Hate, Not) and Three-class (Hate speech, Offensive language, None)
Details of task: Hatespeech detection on social media in English, including 10 categories: African, Islam, Jewish, LGBTQ, Women, Refugee, Arab, Caucasian, Hispanic, Asian
Size of dataset: 20148
Percentage abusive: 57%
Language: English
Level of annotation: Posts
Platform: Twitter and Gab
Medium: Text
Reference: Mathew, B., Saha, P., Yimam, S. M., Biemann, C., Goyal, P., & Mukherjee, A. (2020). Hatexplain: A benchmark dataset for explainable hate speech detection. arXiv preprint arXiv:2012.10289.

French

CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech (French)

Link to publication: https://www.aclweb.org/anthology/P19-1271.pdf
Link to data: https://github.com/marcoguerini/CONAN
Task description: Binary (Islamophobic / not), Multi-topic (Culture, Economics, Crimes, Rapism, Terrorism, Women Oppression, History, Other/generic)
Details of task: Islamophobia
Size of dataset: 1,719
Percentage abusive: 1
Language: French
Level of annotation: Posts
Platform: Synthetic / Facebook
Medium: Text
Reference: Chung, Y., Kuzmenko, E., Tekiroglu, S. and Guerini, M., 2019. CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence, Italy: Association for Computational Linguistics, pp.2819-2829.

Multilingual and Multi-Aspect Hate Speech Analysis (French)

Link to publication: https://arxiv.org/abs/1908.11049
Link to data: https://github.com/HKUST-KnowComp/MLMA_hate_speech
Task description: Detailed taxonomy with cross-cutting attributes: Hostility, Directness, Target Attribute, Target Group, How annotators felt on seeing the tweet.
Details of task: Gender, Sexual orientation, Religion, Disability
Size of dataset: 4,014
Percentage abusive: 0.72
Language: French
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Ousidhoum, N., Lin, Z., Zhang, H., Song, Y. and Yeung, D., 2019. Multilingual and Multi-Aspect Hate Speech Analysis. ArXiv,.

CyberAgressionAdo-v1

Link to publication: (url) - link to the documentation and/or a data statement about the data
Link to data: (url) - direct download is preferred, e.g. a link straight to a .zip file
Task description: The collected conversations have been annotated using a fine-grained tagset including information related to the participant roles, the presence of hate speech, the type of verbal abuse present in the message, and whether utterances use different humour figurative devices (e.g., sarcasm or irony).
Details of task: This dataset allows to perform several subtasks related to the task of online hate detection in a conversational setting (hate speech detection, bullying participant role detection, verbal abuse detection, etc.)
Size of dataset: 19 conversations
Language: French
Level of annotation: exchanged messages
Platform: collected from role playing games mimicking cyberagression situations occuring on private instant messaging platforms.
Medium: text (csv)
Reference: Anaïs Ollagnier, Elena Cabrio, Serena Villata, Catherine Blaya. CyberAgressionAdo-v1: a Dataset of Annotated Online Aggressions in French Collected through a Role-playing Game. Language Resources and Evaluation Conference, Jun 2022, Marseille, France. ⟨hal-03765860⟩

German

DeTox: A Comprehensive Dataset for German Offensive Language and Conversation Analysis

Link to publication: https://aclanthology.org/2022.woah-1.14.pdf
Link to data: https://github.com/hdaSprachtechnologie/detox
Task description: Comprehensive annotation schema (including sentiment, hate speech, type of discrimination, criminal relevance, expression, toxicity, extremism, target, threat)
Details of task: About half of the comments are from coherent comment threads which allows simple conversation analyses. Every comment was annotated by three annotators.
Size of dataset: 10,278
Percentage abusive: 10.85%
Language: German
Level of annotation: Comments
Platform: Twitter
Medium: Text
Reference: Demus, C., Pitz, P., Schütz, M., Probol, N., Siegel, M., and Labudde, L. 2022. DeTox: A Comprehensive Dataset for German Offensive Language and Conversation Analysis. In Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH), pages 143–153, Seattle, Washington (Hybrid). Association for Computational Linguistics.

RP-Mod & RP-Crowd: Moderator- and Crowd-Annotated German News Comment Datasets

Link to publication: https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/file/c9e1074f5b3f9fc8ea15d152add07294-Paper-round2.pdf
Link to data: https://zenodo.org/record/5291339#.Ybr_9VkxkUE
Task description: Binary (Offensive or Not), Multi-class/-label (sexism, racism, threats, insults, profane language, meta, advertisement).
Details of task: The comments originate from a large German newspaper and are annotated by professional moderators (community managers). Additionally, each comment was further annotated by five different crowd-workers.
Size of dataset: 85,000
Percentage abusive: 8.4%
Language: German
Level of annotation: Comments
Platform: German Newspaper (Rheinische Post)
Medium: Text
Reference: Assenmacher, D., Niemann, M., Müller, K., Seiler, M., Riehle, D. M., & Trautmann, H. (2021). RP-Mod & RP-Crowd: Moderator- and crowd-annotated german news comment datasets. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmark.

Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis

Link to publication: https://arxiv.org/pdf/1701.08118.pdf
Link to data: https://github.com/UCSM-DUE/IWG_hatespeech_public
Task description: Binary (Anti-refugee hate, None)
Details of task: Refugees
Size of dataset: 469
Percentage abusive: NA
Language: German
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Ross, B., Rist, M., Carbonell, G., Cabrera, B., Kurowsky, N. and Wojatzki, M., 2017. Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis. ArXiv,.

Link to publication: https://pdfs.semanticscholar.org/23dc/df7c7e82807445afd9f19474fc0a3d8169fe.pdf
Link to data: http://ub-web.de/research/
Task description: Hierarchical (Anti-foreigner prejudice, split into (1) slightly offensive/offensive and (2) explicitly/substantially offensive). 6 targets (Foreigner, Government, Press, Community, Other, Unknown)
Details of task: Anti-foreigner prejudice
Size of dataset: 5,836
Percentage abusive: 0.11
Language: German
Level of annotation: Posts
Platform: Facebook
Medium: Text
Reference: Bretschneider, U. and Peters, R., 2017. Detecting Offensive Statements towards Foreigners in Social Media. In: Proceedings of the 50th Hawaii International Conference on System Sciences.

GermEval 2018

Link to publication: https://www.researchgate.net/publication/327914386_Overview_of_the_GermEval_2018_Shared_Task_on_the_Identification_of_Offensive_Language
Link to data: https://github.com/uds-lsv/GermEval-2018-Data
Task description: Branching structure: Binary (Offense, Other), 3 levels within Offense (Abuse, Insult, Profanity)
Details of task: Group-directed + Incivility
Size of dataset: 8,541
Percentage abusive: 0.34
Language: German
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Wiegand, M., Siegel, M. and Ruppenhofer, J., 2018. Overview of the GermEval 2018 Shared Task on the Identification of Offensive Language. In: Proceedings of GermEval 2018, 14th Conference on Natural Language Processing (KONVENS 2018). Vienna, Austria: Research Gate.

Overview of the HASOC track at FIRE 2019: Hate Speech and Offensive Content Identification in Indo-European Languages

Link to publication: https://dl.acm.org/doi/pdf/10.1145/3368567.3368584?download=true
Link to data: https://hasocfire.github.io/hasoc/2019/dataset.html
Task description: A: Hate / Offensive or neither, B: Hatespeech, Offensive, or Profane
Details of task: Group-directed + Person-directed
Size of dataset: 4,669
Percentage abusive: 0.24
Language: German
Level of annotation: Posts
Platform: Twitter and Facebook
Medium: Text
Reference: Mandl, T., Modha, S., Majumder, P., Patel, D., Dave, M., Mandlia, C. and Patel, A., 2019. Overview of the HASOC track at FIRE 2019. In: Proceedings of the 11th Forum for Information Retrieval Evaluation,.

GAHD: A German Adversarial Hate Speech Dataset

Link to publication: https://arxiv.org/abs/2403.19559
Link to data: https://github.com/jagol/gahd
Task description: Binary hate speech detection (“hate speech”, “not-hate speech”)
Details of task: Consists of adversarial and contrastive examples
Size of dataset: 10,996 texts
Percentage abusive: 42.4%
Language: German
Level of annotation: Post/Sentence
Platform: Synthetic data and news sentences
Medium: Text
Reference: Goldzycher, J., Röttger, P., and Schneider, G., 2024. Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset. To appear in the Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL 2024), Mexico, Mexico City, June 17–19.

Greek

Deep Learning for User Comment Moderation, Flagged Comments

Link to publication: https://www.aclweb.org/anthology/W17-3004
Link to data: http://www.straintek.com/data/
Task description: Binary (Flagged, Not)
Details of task: Flagged content
Size of dataset: 1,450,000
Percentage abusive: 0.34
Language: Greek
Level of annotation: Posts
Platform: Gazetta
Medium: text
Reference: Pavlopoulos, J., Malakasiotis, P. and Androutsopoulos, I., 2017. Deep Learning for User Comment Moderation. In: Proceedings of the First Workshop on Abusive Language Online. Vancouver, Canada: Association for Computational Linguistics, pp.25-35.

Deep Learning for User Comment Moderation, Moderated Comments

Link to publication: https://www.aclweb.org/anthology/W17-3004
Link to data: http://www.straintek.com/data/
Task description: Binary (Flagged, Not)
Details of task: Flagged content
Size of dataset: 1,500
Percentage abusive: 0.22
Language: Greek
Level of annotation: Posts
Platform: Gazetta
Medium: text
Reference: Pavlopoulos, J., Malakasiotis, P. and Androutsopoulos, I., 2017. Deep Learning for User Comment Moderation. In: Proceedings of the First Workshop on Abusive Language Online. Vancouver, Canada: Association for Computational Linguistics, pp.25-35.

Offensive Language Identification in Greek

Link to publication: https://arxiv.org/pdf/2003.07459v1.pdf
Link to data: https://sites.google.com/site/offensevalsharedtask/home
Task description: Branching structure of tasks: Binary (Offensive, Not), Within Offensive (Target, Not), Within Target (Individual, Group, Other)
Details of task: Group-directed + Person-directed
Size of dataset: 4779
Percentage abusive: 0.29
Language: Greek
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Pitenis, Z., Zampieri, M. and Ranasinghe, T., 2020. Offensive Language Identification in Greek. ArXiv.
Dataset reader: 🤗 strombergnlp/offenseval_2020

Hindi / Hindi-English

Hostility Detection Dataset in Hindi

Link to publication: https://arxiv.org/pdf/2011.03588.pdf
Link to data: https://competitions.codalab.org/competitions/26654
Task description: Branching structure of tasks: Binary (Hostile, Not Hostile), Multi-tags within Hostile (Fake News, Hate, Offense, Defame)
Details of task: Hostility detection
Size of dataset: 8,192
Percentage abusive: 47%
Language: Hindi
Level of annotation: Posts
Platform: Twitter, Facebook, WhatsApp
Medium: Text
Reference: Bhardwaj, M., Akhtar, M.S., Ekbal, A., Das, A. and Chakraborty, T., 2020. Hostility detection dataset in hindi. arXiv preprint arXiv:2011.03588.

Aggression-annotated Corpus of Hindi-English Code-mixed Data

Link to publication: https://arxiv.org/pdf/1803.09402
Link to data: https://github.com/kraiyani/Facebook-Post-Aggression-Identification
Task description: 3 part hierachy for hate (None, Covert Aggression, Overt Aggression), 4 part target categorisation (Physical threat, Sexual threat, Identity threat, Non-threatening aggression), 3-part discursive role categorisation (Attack, Defend, Abet)
Details of task: Numerous sub-categorizations
Size of dataset: 18,000
Percentage abusive: 0.06
Language: Hindi-English
Level of annotation: Posts
Platform: Facebook
Medium: Text
Reference: Kumar, R., Reganti, A., Bhatia, A. and Maheshwari, T., 2018. Aggression-annotated Corpus of Hindi-English Code-mixed Data. ArXiv,.

Aggression-annotated Corpus of Hindi-English Code-mixed Data

Link to publication: https://arxiv.org/pdf/1803.09402
Link to data: https://github.com/kraiyani/Facebook-Post-Aggression-Identification
Task description: 3 part hierachy for hate (None, Covert Aggression, Overt Aggression), 4 part target categorisation (Physical threat, Sexual threat, Identity threat, Non-threatening aggression), 3-part discursive role categorisation (Attack, Defend, Abet)
Details of task: Numerous sub-categorizations
Size of dataset: 21,000
Percentage abusive: 0.27
Language: Hindi-English
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Kumar, R., Reganti, A., Bhatia, A. and Maheshwari, T., 2018. Aggression-annotated Corpus of Hindi-English Code-mixed Data. ArXiv,.

Did You Offend Me? Classification of Offensive Tweets in Hinglish Language

Link to publication: https://www.aclweb.org/anthology/W18-5118
Link to data: https://github.com/pmathur5k10/Hinglish-Offensive-Text-Classification
Task description: Hierarchy (Not Offensive, Abusive, Hate)
Details of task: Sexism
Size of dataset: 3,189
Percentage abusive: 0.65
Language: Hindi-English
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Mathur, P., Sawhney, R., Ayyar, M. and Shah, R., 2018. Did you offend me? Classification of Offensive Tweets in Hinglish Language. In: Proceedings of the 2nd Workshop on Abusive Language Online (ALW2). Brussels, Belgium: Association for Computational Linguistics, pp.138-148.

Link to publication: https://www.aclweb.org/anthology/W18-1105
Link to data: https://github.com/deepanshu1995/HateSpeech-Hindi-English-Code-Mixed-Social-Media-Text
Task description: Binary (Hate, Not)
Details of task: Hate per se
Size of dataset: 4,575
Percentage abusive: 0.36
Language: Hindi-English
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Bohra, A., Vijay, D., Singh, V., Sarfaraz Akhtar, S. and Shrivastava, M., 2018. A Dataset of Hindi-English Code-Mixed Social Media Text for Hate Speech Detection. In: Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media. New Orleans, Louisiana: Association for Computational Linguistics, pp.36-41.

Overview of the HASOC track at FIRE 2019: Hate Speech and Offensive Content Identification in Indo-European Languages

Link to publication: https://dl.acm.org/doi/pdf/10.1145/3368567.3368584?download=true
Link to data: https://hasocfire.github.io/hasoc/2019/dataset.htm
Task description: A: Hate, Offensive or Neither, B: Hatespeech, Offensive, or Profane, C: Targeted or Untargeted
Details of task: Group-directed + Person-directed
Size of dataset: 5,983
Percentage abusive: 0.51
Language: Hindi
Level of annotation: Posts
Platform: Twitter and Facebook
Medium: Text
Reference: Mandl, T., Modha, S., Majumder, P., Patel, D., Dave, M., Mandlia, C. and Patel, A., 2019. Overview of the HASOC track at FIRE 2019. In: Proceedings of the 11th Forum for Information Retrieval Evaluation,.

Indonesian

Hate Speech Detection in the Indonesian Language: A Dataset and Preliminary Study

Link to publication: https://ieeexplore.ieee.org/document/8355039
Link to data: https://github.com/ialfina/id-hatespeech-detection
Task description: Binary (Hate, Not)
Details of task: Hate per se
Size of dataset: 713
Percentage abusive: 0.36
Language: Indonesian
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Alfina, I., Mulia, R., Fanany, M. and Ekanata, Y., 2017. Hate Speech Detection in the Indonesian Language: A Dataset and Preliminary Study. In: International Conference on Advanced Computer Science and Information Systems. pp.233-238.

Multi-Label Hate Speech and Abusive Language Detection in Indonesian Twitter

Link to publication: https://www.aclweb.org/anthology/W19-3506
Link to data: https://github.com/okkyibrohim/id-multi-label-hate-speech-and-abusive-language-detection
Task description: (No hate speech, No hate speech but abusive, Hate speech but no abuse, Hate speech and abuse), within hate, category (Religion/creed, Race/ethnicity, Physical/disability, Gender/sexual orientation, Other invective/slander), within hate, strength (Weak, Moderate and Strong)
Details of task: Religion, Race, Disability, Gender
Size of dataset: 13,169
Percentage abusive: 0.42
Language: Indonesian
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Okky Ibrohim, M. and Budi, I., 2019. Multi-label Hate Speech and Abusive Language Detection in Indonesian Twitter. In: Proceedings of the Third Workshop on Abusive Language Online. Florence, Italy: Association for Computational Linguistics, pp.46-57.

Link to publication: https://www.sciencedirect.com/science/article/pii/S1877050918314583
Link to data: https://github.com/okkyibrohim/id-abusive-language-detection
Task description: Hierarchical (Not abusive, Abusive but not offensive, Offensive)
Details of task: Incivility
Size of dataset: 2,016
Percentage abusive: 0.54
Language: Indonesian
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Ibrohim, M. and Budi, I., 2018. A Dataset and Preliminaries Study for Abusive Language Detection in Indonesian Social Media. Procedia Computer Science, 135, pp.222-229.

Korean

BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection__

Link to publication: https://www.aclweb.org/anthology/2020.socialnlp-1.4
Link to data: https://github.com/kocohub/korean-hate-speech
Task description: Binary (Gender bias, No gender bias), Ternary (Gender bias, Other biases, None), Ternary (Hate, Offensive, None)
Details of task: Person/Group-directed, Gender/Sexual orientation, Sexism, Harmfulness/Toxicity
Size of dataset: 9,381
Percentage abusive: 33.87 (Bias), 57.77 (Toxicity)
Language: Korean
Level of annotation: Comments
Platform: NAVER entertainment news
Medium: Text
Reference: Moon, J., Cho, W. I., and Lee, J., 2020. BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection. In: Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media Month: July. Online: Association for Computational Linguistics, pp.25-31.

Latvian

Latvian newspaper user comment dataset

Link to publication: https://aclanthology.org/2021.hackashop-1.14.pdf
Link to data: https://www.clarin.si/repository/xmlui/handle/11356/1407
Task description: Binary (Deleted, Not)
Details of task: Flagged content performmed by the real newspaper moderators
Size of dataset: 12M
Percentage abusive: ~10%
Language: Latvian
Level of annotation: Posts
Platform: Newspaper comments
Medium: Text
Reference: Senja Pollak, Marko Robnik-Šikonja, Matthew Purver, Michele Boggia, Ravi Shekhar, Marko Pranjić, Salla Salmela, Ivar Krustok, Tarmo Paju, Carl-Gustav Linden, Leo Leppänen, Elaine Zosa, Matej Ulčar, Linda Freiental, Silver Traat, Luis Adrián Cabrera-Diego, Matej Martinc, Nada Lavrač, Blaž Škrlj, Martin Žnidaršič, Andraž Pelicon, Boshko Koloski, Vid Podečan, Janez Kranjc, Shane Sheehan, Emanuela Boros, Jose Moreno, Antoine Doucet, Hannu Toivonen (2021). EMBEDDIA Tools, Datasets and Challenges: Resources and Hackathon Contributions. Proceedings of the Hackashop on News Media Content Analysis and Automated Report Generation (EACL).

Italian

An Italian Twitter Corpus of Hate Speech against Immigrants

Link to publication: https://www.aclweb.org/anthology/L18-1443
Link to data: https://github.com/msang/hate-speech-corpus
Task description: Binary (Immigrants/Roma/Muslims, Not), additional categories. Within Hate, Intensity measurement (Aggressiveness: No, Weak, Strong, Offensiveness: No, Weak, Strong, Irony: No, Yes, Stereotype: No, Yes, Incitement degree: 0-4)
Details of task: Immigrants, Roma and Muslims + numerous sub-categorizations
Size of dataset: 1,827
Percentage abusive: 0.13
Language: Italian
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Sanguinetti, M., Poletto, F., Bosco, C., Patti, V. and Stranisci, M., 2018. An Italian Twitter Corpus of Hate Speech against Immigrants. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). Miyazaki, Japan: European Language Resources Association (ELRA).

Overview of the EVALITA 2018 Hate Speech Detection Task (Facebook)

Link to publication: http://ceur-ws.org/Vol-2263/paper010.pdf
Link to data: http://www.di.unito.it/~tutreeb/haspeede-evalita18/data.html
Task description: Binary (Hate, Not), Within hate for Facebook only, strength (No hate, Weak hate, Strong hate) and theme ((1) religion, (2) physical and/or mental handicap, (3) socio-economic status, (4) politics, (5) race, (6) sex and gender, (7) Other)
Details of task: Religion, physical and/or mental handicap, socio-economic status, politics, race, sex and gender
Size of dataset: 4,000
Percentage abusive: 0.51
Language: Italian
Level of annotation: Posts
Platform: Facebook
Medium: Text
Reference: Bosco, C., Dell’Orletta, F. and Poletto, F., 2018. Overview of the EVALITA 2018 Hate Speech Detection Task. In: EVALITA 2018-Sixth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. CEUR, pp.1-9.

Overview of the EVALITA 2018 Hate Speech Detection Task (Twitter)

Link to publication: http://ceur-ws.org/Vol-2263/paper010.pdf
Link to data: http://www.di.unito.it/~tutreeb/haspeede-evalita18/data.html
Task description: Binary (Hate, Not), Within Hate For Twitter only Intensity (1-4 rating), Aggressiveness (No, Weak, Strong), Offensiveness (No, Weak, Strong), Irony (Yes, No)
Details of task: Group-directed
Size of dataset: 4,000
Percentage abusive: 0.32
Language: Italian
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Bosco, C., Dell’Orletta, F. and Poletto, F., 2018. Overview of the EVALITA 2018 Hate Speech Detection Task. In: EVALITA 2018-Sixth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. CEUR, pp.1-9.

Automatic Misogyny Identification (AMI) at Evalita 2020

Link to publication: http://ceur-ws.org/Vol-2765/paper161.pdf
Link to data: https://github.com/dnozza/ami2020
Task description: Binary (misogyny / not), Binary (aggressive / not), Binary on synthetic fairness test (misogyny / not)
Details of task: Sexism
Size of dataset: 6,000 and 1,961 (synthetic fairness test)
Percentage abusive: 47% and 50% (synthetic fairness test)
Language: Italian
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Fersini, E., Nozza, D., and Rosso, P., 2020. AMI @ EVALITA2020: Automatic Misogyny Identification. In: Proceedings of the 7th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA 2020).

CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech (Italian)

Link to publication: https://www.aclweb.org/anthology/P19-1271.pdf
Link to data: https://github.com/marcoguerini/CONAN
Task description: Binary (Islamophobic, Not), Multi-topic (Culture, Economics, Crimes, Rapism, Terrorism, Women Oppression, History, Other/generic)
Details of task: Islamophobia
Size of dataset: 1,071
Percentage abusive: 1
Language: Italian
Level of annotation: Posts
Platform: Synthetic / Facebook
Medium: Text
Reference: Chung, Y., Kuzmenko, E., Tekiroglu, S. and Guerini, M., 2019. CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence, Italy: Association for Computational Linguistics, pp.2819-2829.

Creating a WhatsApp Dataset to Study Pre-teen Cyberbullying

Link to publication: https://www.aclweb.org/anthology/W18-5107
Link to data: https://github.com/dhfbk/WhatsApp-Dataset
Task description: Binary (Cyberbullying, Not)
Details of task: Person-directed
Size of dataset: 14,600
Percentage abusive: 0.08
Language: Italian
Level of annotation: Posts, structured into 10 chats, with token level information
Platform: Synthetic / Whatsapp
Medium: Text
Reference: Sprugnoli, R., Menini, S., Tonelli, S., Oncini, F. and Piras, E., 2018. Creating a WhatsApp Dataset to Study Pre-teen Cyberbullying. In: Proceedings of the 2nd Workshop on Abusive Language Online (ALW2) Month: October. Brussels, Belgium: Association for Computational Linguistics, pp.51-59.

Polish

Results of the PolEval 2019 Shared Task 6:First Dataset and Open Shared Task for Automatic Cyberbullying Detection in Polish Twitter

Link to publication: http://poleval.pl/files/poleval2019.pdf
Link to data: http://poleval.pl/tasks/task6
Task description: Harmfulness score (three values), Multilabel from seven phenomena
Details of task: Person-directed
Size of dataset: 10,041
Percentage abusive: 0.09
Language: Polish
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Ogrodniczuk, M. and Kobyliński, L., 2019. Results of the PolEval 2019 Shared Task 6: First Dataset and Open Shared Task for Automatic Cyberbullying Detection in Polish Twitter. In: Proceedings of the PolEval 2019 Workshop. Warszawa: Institute of Computer Science, Polish Academy of Sciences.

Portuguese

Toxic Language Dataset for Brazilian Portuguese (ToLD-Br)

Link to publication: https://arxiv.org/abs/2010.04543
Link to data: https://github.com/JAugusto97/ToLD-Br
Task description: Multiclass (LGBTQ+phobia, Insult, Xenophobia, Misogyny, Obscene, Racism)
Details of task: Three annotators per example, demographically diverse selected annotators.
Size of dataset: 21.000
Percentage abusive: 44%
Language: Portuguese
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: João A. Leite, Diego F. Silva, Kalina Bontcheva, Carolina Scarton (2020): Toxic Language Detection in Social Media for Brazilian Portuguese: New Dataset and Multilingual Analysis. AACL-IJCNLP 2020

A Hierarchically-Labeled Portuguese Hate Speech Dataset

Link to publication: https://www.aclweb.org/anthology/W19-3510
Link to data: https://b2share.eudat.eu/records/9005efe2d6be4293b63c3cffd4cf193e
Task description: Binary (Hate, Not), Multi-level (81 categories, identified inductively; categories have different granularities and content can be assigned to multiple categories at once)
Details of task: Multiple identities inductively categorized
Size of dataset: 3,059
Percentage abusive: 0.32
Language: Portuguese
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Fortuna, P., Rocha da Silva, J., Soler-Company, J., Warner, L. and Nunes, S., 2019. A Hierarchically-Labeled Portuguese Hate Speech Dataset. In: Proceedings of the Third Workshop on Abusive Language Online. Florence, Italy: Association for Computational Linguistics, pp.94-104.

Offensive Comments in the Brazilian Web: A Dataset and Baseline Results

Link to publication: http://www.each.usp.br/digiampietri/BraSNAM/2017/p04.pdf
Link to data: https://github.com/rogersdepelle/OffComBR
Task description: Binary (Offensive, Not), Target (Xenophobia, homophobia, sexism, racism, cursing, religious intolerance)
Details of task: Religion/creed, Race/ethnicity, Physical/disability, Gender/sexual orientation
Size of dataset: 1,250
Percentage abusive: 0.33
Language: Portuguese
Level of annotation: Posts
Platform: g1.globo.com
Medium: Text
Reference: de Pelle, R. and Moreira, V., 2017. Offensive Comments in the Brazilian Web: A Dataset and Baseline Results. In: VI Brazilian Workshop on Social Network Analysis and Mining. SBC.

Offensive Language Identification Dataset for Brazilian Portuguese (OLID-BR)

Link to publication: https://link.springer.com/article/10.1007/s10579-023-09657-0
Link to data: https://huggingface.co/datasets/dougtrajano/olid-br
Task description: Binary (Offensive, Not), Multi-label (11 labels), Targeted or Not, Target Type (individual, group, other), Offensive Spans Identification
Details of task: Religion/creed, Race/ethnicity, Physical/disability, Gender/sexual orientation, Profanity/obscene.
Size of dataset: 7,943
Percentage abusive: 0.85
Language: Portuguese
Level of annotation: Posts
Platform: Twitter, YouTube, and other Portuguese Datasets
Medium: Text
Reference: Trajano, D., Bordini, R.H. & Vieira, R. OLID-BR: offensive language identification dataset for Brazilian Portuguese. Lang Resources & Evaluation (2023).

Russian

Link to publication: https://github.com/alla-g/toxicity-detection-thesis/blob/main/toxicity_corpus/DATASTATEMENT.md
Link to data: https://github.com/alla-g/toxicity-detection-thesis/blob/main/toxicity_corpus/russian_distorted_toxicity.tsv
Task description: Toxicity - binary (1 == toxic, 0 == not toxic), Distortion - binary (1 == has distortion, 0 == does not have distortion),
Details of task: 1) multitask Russian toxicity detection with distortion detection as an auxiliary task; 2) testing toxicity classifiers on parallel distorted and manually corrected data
Size of dataset: 3000 texts: 561 toxic, 2439 not toxic; 126 distorted, 2874 not distorted.
Percentage abusive: 18.7%
Language: Russian
Level of annotation: comment
Platform: VKontakte
Medium: text
Reference: Gorbunova, A. (2022). Automatic Toxic Comment Detection in Social Media for Russian [Unpublished bachelor’s thesis]. National Research University Higher School of Economics.

Reducing Unintended Identity Bias in Russian Hate Speech Detection

Link to publication: https://aclanthology.org/2020.alw-1.8.pdf
Link to data: License Required (Last checked 17/01/2022)
Task description: Binary (Hate, Not)
Details of task: Toxicity, Harassment, Sexism, Homophobia, Nationalism
Size of dataset: 100,000
Percentage abusive: NA
Language: Russian
Level of annotation: Posts
Platform: Youtube
Medium: Text
Reference: Zueva, Nadezhda, et al, Oct. 2020. Reducing Unintended Identity Bias in Russian Hate Speech Detection. In: Proceedings of the Fourth Workshop on Online Abuse and Harms, pages 65–69

Detection of Abusive Speech for Mixed Sociolects of Russian and Ukrainian Languages

Link to publication: https://nlp.fi.muni.cz/raslan/2018/paper04-Andrusyak.pdf
Link to data: https://github.com/bohdan1/AbusiveLanguageDataset
Task description: Binary (True == Abusive, False == Not)
Details of task: Multilingual, Abusive Words, Political
Size of dataset: 2,000
Percentage abusive: 0.33
Language: Surzhyk (Russian & Ukranian)
Level of annotation: Posts
Platform: Youtube
Medium: Text
Reference: Andrusyak, B., Rimel, M. and Kern, R., 2018. Detection of Abusive Speech for Mixed Sociolects of Russian and Ukrainian Languages. In: Proceedings of Recent Advances in Slavonic Natural Language Processing, RASLAN 2018, pp. 77–84, 2018.

Russian South Park

Link to publication: https://aclanthology.org/2021.bsnlp-1.3/
Link to data: https://github.com/Sariellee/Russan-Hate-speech-Recognition
Task description: Binary (abusive, non-abusive)
Details of task: Abusive language in Russian South Park scripts
Size of dataset: 1400
Percentage abusive: 22.2%
Language: Russian
Level of annotation: Sentence
Platform: TV Subtitles
Medium: text
Reference: Saitov & Derczynski, 2021. “Abusive Language Recognition in Russian”. Proceedings of the 8th BSNLP Workshop on Balto-Slavic Natural Language Processing, ACL

Slovene

Datasets of Slovene and Croatian Moderated News Comments

Link to publication: https://www.aclweb.org/anthology/W18-5116
Link to data: http://hdl.handle.net/11356/1201
Task description: Binary (Deleted, Not)
Details of task: Flagged content
Size of dataset: 7,600,000
Percentage abusive: 0.08
Language: Slovene
Level of annotation: Posts
Platform: MMC RTV website
Medium: Text
Reference: Ljubešić, N., Erjavec, T. and Fišer, D., 2018. Datasets of Slovene and Croatian Moderated News Comments. In: Proceedings of the 2nd Workshop on Abusive Language Online (ALW2). Brussels, Belgium: Association for Computational Linguistics, pp.124-131.

Spanish

Overview of MEX-A3T at IberEval 2018: Authorship and Aggressiveness Analysis in Mexican Spanish Tweets

Link to publication: http://ceur-ws.org/Vol-2150/overview-mex-a3t.pdf
Link to data: https://mexa3t.wixsite.com/home/aggressive-detection-track
Task description: Binary (Aggressive, Not)
Details of task: Group-directed
Size of dataset: 11,000
Percentage abusive: 0.32
Language: Spanish
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Alvarez-Carmona, M., Guzman-Falcon, E., Montes-y-Gomez, M., Escalante, H., Villasenor-Pineda, L., Reyes-Meza, V. and Rico-Sulayes, A., 2018. Overview of MEX-A3T at IberEval 2018: Authorship and aggressiveness analysis in Mexican Spanish tweets. In: Proceedings of the Third Workshop on Evaluation of Human Language Technologies for Iberian Languages (IberEval 2018).

Overview of the Task on Automatic Misogyny Identification at IberEval 2018 (Spanish)

Link to publication: http://ceur-ws.org/Vol-2150/overview-AMI.pdf
Link to data: https://amiibereval2018.wordpress.com/important-dates/data/
Task description: Binary (Misogyny, Not), 5 categories (Stereotype, Dominance, Derailing, Sexual harassment, Discredit), Target of misogyny (Active or Passive)
Details of task: Sexism
Size of dataset: 4,138
Percentage abusive: 0.5
Language: Spanish
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Fersini, E., Rosso, P. and Anzovino, M., 2018. Overview of the Task on Automatic Misogyny Identification at IberEval 2018. In: Proceedings of the Third Workshop on Evaluation of Human Language Technologies for Iberian Languages (IberEval 2018).

hatEval, SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter (Spanish)

Link to publication: https://www.aclweb.org/anthology/S19-2007
Link to data: competitions.codalab.org/competitions/19935
Task description: Branching structure of tasks: Binary (Hate, Not), Within Hate (Group, Individual), Within Hate (Agressive, Not)
Details of task: Group-directed + Person-directed
Size of dataset: 6,600
Percentage abusive: 0.4
Language: Spanish
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Basile, V., Bosco, C., Fersini, E., Nozza, D., Patti, V., Pardo, F., Rosso, P. and Sanguinetti, M., 2019. SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter. In: Proceedings of the 13th International Workshop on Semantic Evaluation. Minneapolis, Minnesota: Association for Computational Linguistics, pp.54-63.

Turkish

Large-Scale Hate Speech Detection with Cross-Domain Transfer

Link to publication: https://aclanthology.org/2022.lrec-1.238/
Link to data: https://github.com/avaapm/hatespeech
Task description: Three-class (Hate speech, Offensive language, None)
Details of task: Hate speech detection on social media (Twitter) including 5 target groups (gender, race, religion, politics, sports)
Size of dataset: 100k (7325 hate, 27140 offensive, 65535 none)
Percentage abusive: 34.5%
Language: Turkish
Level of annotation: Posts
Platform: Twitter
Medium: Text and image
Reference: Cagri Toraman, Furkan Şahinuç, Eyup Yilmaz. 2022. Large-Scale Hate Speech Detection with Cross-Domain Transfer. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 2215–2225, Marseille, France. European Language Resources Association.

Link to publication: https://coltekin.github.io/offensive-turkish/troff.pdf
Link to data: https://sites.google.com/site/offensevalsharedtask/home
Task description: Branching structure of tasks: Binary (Hate, Not), Within Hate (Group, Individual), Within Hate (Agressive, Not)
Details of task: Group-directed + Person-directed
Size of dataset: 36232
Percentage abusive: 0.19
Language: Turkish
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Çöltekin, C., 2020. A Corpus of Turkish Offensive Language on Social Media. In: Proceedings of the 12th International Conference on Language Resources and Evaluation.
Dataset reader: 🤗 strombergnlp/offenseval_2020

Ukranian

Detection of Abusive Speech for Mixed Sociolects of Russian and Ukrainian Languages

Link to publication: https://nlp.fi.muni.cz/raslan/2018/paper04-Andrusyak.pdf
Link to data: https://github.com/bohdan1/AbusiveLanguageDataset
Task description: Binary (True == Abusive, False == Not)
Details of task: Multilingual, Abusive Words, Political
Size of dataset: 2,000
Percentage abusive: 0.33
Language: Surzhyk (Russian & Ukranian)
Level of annotation: Posts
Platform: Youtube
Medium: Text
Reference: Andrusyak, B., Rimel, M. and Kern, R., 2018. Detection of Abusive Speech for Mixed Sociolects of Russian and Ukrainian Languages. In: Proceedings of Recent Advances in Slavonic Natural Language Processing, RASLAN 2018, pp. 77–84, 2018.

Urdu

Hate-Speech and Offensive Language Detection in Roman Urdu

Link to publication: https://www.aclweb.org/anthology/2020.emnlp-main.197/
Link to data: https://github.com/haroonshakeel/roman_urdu_hate_speech
Task description: There are 2 subtasks, Coarse-grained Classification(Hate-Offensive vs Normal) and Fine-grained classification( Abusive/Offensive, Sexism, Religious Hate, Profane, Normal)
Details of task: Binary classification + Hate-Offensive label is further broken down into 4 fine-grained labels
Size of dataset: 10041
Percentage abusive: 0.24%
Language: Urdu-English
Level of annotation: Posts
Platform: Twitter
Medium: Text
Reference: Hammad Rizwan, Muhammad Haroon Shakeel, and Asim Karim. 2020. Hate-speech and offensive language detection in Roman Urdu. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2512–2522, Online. Association for Computational Linguistics.

Lists of abusive keywords

The Weaponized Word
- “The Weaponized Word offers several thousand discriminatory, derogatory and threatening terms across 125+ languages, available through a RESTful API. Access is free for most academic researchers and registered humanitarian nonprofits.”
- Data link: weaponizedword.org
Hurtlex
- “HurtLex is a lexicon of offensive, aggressive, and hateful words in over 50 languages. The words are divided into 17 categories, plus a macro-category indicating whether there is stereotype involved.”
- Data link: github.com/valeriobasile/hurtlex
- Reference: Hurtlex: A Multilingual Lexicon of Words to Hurt, Proc. CLiC-it 2018
Gorrell et al.
- Data link: http://staffwww.dcs.shef.ac.uk/people/G.Gorrell/publications-materials/abuse-terms.txt
- Reference: Twits, Twats and Twaddle: Trends in Online Abuse towards UK Politicians, Proc. ICWSM
- You can also use the GATE abuse tagger, available at https://cloud.gate.ac.uk/shopfront/displayItem/gate-hate
Wiegand et al.
- Data link: https://github.com/uds-lsv/lexicon-of-abusive-words
- Reference: Inducing a Lexicon of Abusive Words – A Feature-Based Approach, Proc. NAACL-HLT 2018
Chandrasekharan et al.
- Data link: Reddit hate lexicon
- Reference: You can’t stay here: the efficacy of Reddit’s 2015 ban examined through hate speech, Proc. ACL Hum-Comput Interact.
Jiang et al.
- SexHateLex is a Chinese lexicon of hateful and sexist words.
- Data link: SexHateLex
- Size of lexicon: 3,016
- Reference: SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection, Journal of OSNEM, Vol.27, 2022, 100182, ISSN 2468-6964.

How to Contribute

We accept entries to our catalogue based on pull requests to the README.md file. The dataset must be avaliable for download to be included in the list. If you want to add an entry, follow these steps!

Please send just one dataset addition/edit at a time - edit it in, then save. This will make everyone’s life easier (including yours!)
Go to the README.md file and click the edit button in the top right corner of the file.

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Edit the markdown file. Please first go the correct language. The items are then sorted by their publication date (newest first). Add your item by copy and pasting the following template and adding all the details:

#### Title
* Link to publication: [url](url) - link to the documentation and/or a data statement about the data
* Link to data: [url](url) - direct download is preferred, e.g. a link straight to a .zip file
* Task description: How the task is framed in this data, e.g. "Binary (Hate, Not)", "Hierarchical", "Three-class (Hate speech, Offensive language, None)"
* Details of task: Free-text description of the task this data models, e.g. "Misogyny detection on social media in Danish"
* Size of dataset: Give the number of instances of abusive/non-abusive/other items
* Percentage abusive: e.g. 1.2%
* Language: e.g. Arabic
* Level of annotation: What is an "instance", in this dataset? e.g. Posts, User, Conversation, ... 
* Platform: e.g. twitter, snapchat, ..
* Medium: text / image / audio / ...
* Reference: Give a bibliographic reference for the data (if there is one), with title, author, year, venue etc

Check the “Preview Changes” tab to confirm everything is good to go!
If you’re ready to submit, propose the changes. Make sure you give some brief detail on the proposed change.

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Submit the pull request on the next page when prompted.

This page is http://hatespeechdata.com/.

Hate Speech Dataset Catalogue

Datasets Table of Contents

List of datasets

Albanian

Detecting Abusive Albanian

Arabic

Let-Mi: An Arabic Levantine Twitter Dataset for Misogynistic Language

Are They our Brothers? Analysis and Detection of Religious Hate Speech in the Arabic Twittersphere

Multilingual and Multi-Aspect Hate Speech Analysis (Arabic)

L-HSAB: A Levantine Twitter Dataset for Hate Speech and Abusive Language

Abusive Language Detection on Arabic Social Media (Twitter)

Abusive Language Detection on Arabic Social Media (Al Jazeera)

Dataset Construction for the Detection of Anti-Social Behaviour in Online Communication in Arabic

Bengali

Hate Speech Detection in the Bengali language: A Dataset and its Baseline Evaluation

Chinese

SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection

Croatian

CoRAL: a Context-aware Croatian Abusive Language Dataset

Datasets of Slovene and Croatian Moderated News Comments

Automating News Comment Moderation with Limited Resources: Benchmarking in Croatian and Estonian

Danish

Offensive Language and Hate Speech Detection for Danish

BAJER: Misogyny in Danish

Dutch

The Dutch Abusive Language Corpus v1.0 (DALC v1.0)

English

Not All Counterhate Tweets Elicit the Same Replies: A Fine-Grained Analysis

Pinpointing Fine-Grained Relationships between Hateful Tweets and Replies

Large-Scale Hate Speech Detection with Cross-Domain Transfer

ConvAbuse

Measuring Hate Speech

Learning From the Worst (Dynamically generated hate speech dataset)

The ‘Call me sexist, but’ sexism dataset

Hate Towards the Political Opponent: A Twitter Corpus Study of the 2020 US Elections on the Basis of Offensive Speech and Stance Detection__

AbuseEval v1.0

Do You Really Want to Hurt Me? Predicting Abusive Swearing in Social Media

Multimodal Meme Dataset (MultiOFF) for Identifying Offensive Content in Image and Text

Hatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-based Hate

HateCheck: Functional Tests for Hate Speech Detection Models

Semeval-2021 Task 5: Toxic Spans Detection

ToxiSpanSE: An Explainable Toxicity Detection in Code Review Comments

Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech

HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection

ALONE: A Dataset for Toxic Behavior among Adolescents on Twitter

Towards a Comprehensive Taxonomy and Large-Scale Annotated Corpus for Online Slur Usage

Multimodal Meme Dataset (MultiOFF) for Identifying Offensive Content in Image and Text

Predicting the Type and Target of Offensive Posts in Social Media

Nuanced metrics for measuring unintended bias with real data for text classification

Introducing CAD: the Contextual Abuse Dataset

Automated Hate Speech Detection and the Problem of Offensive Language

Hate Speech Dataset from a White Supremacy Forum

Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter

Detecting Online Hate Speech Using Context Aware Models

The Gab Hate Corpus: A collection of 27k posts annotated for hate speech

Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech Detection on Twitter

When Does a Compliment Become Sexist? Analysis and Classification of Ambivalent Sexism Using Twitter Data

Overview of the Task on Automatic Misogyny Identification at IberEval 2018 (English)

CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech (English)

Characterizing and Detecting Hateful Users on Twitter

A Benchmark Dataset for Learning to Intervene in Online Hate Speech (Gab)

A Benchmark Dataset for Learning to Intervene in Online Hate Speech (Reddit)

Multilingual and Multi-Aspect Hate Speech Analysis (English)

Exploring Hate Speech Detection in Multimodal Publications

Predicting the Type and Target of Offensive Posts in Social Media

hatEval, SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter (English)

Peer to Peer Hate: Hate Speech Instigators and Their Targets

Overview of the HASOC track at FIRE 2019: Hate Speech and Offensive Content Identification in Indo-European Languages

Detecting East Asian Prejudice on Social media

Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior

A Large Labeled Corpus for Online Harassment Research

Ex Machina: Personal Attacks Seen at Scale, Personal attacks

Ex Machina: Personal Attacks Seen at Scale, Toxicity

Detecting cyberbullying in online communities (World of Warcraft)

Detecting cyberbullying in online communities (League of Legends)

A Quality Type-aware Annotated Corpus and Lexicon for Harassment Research

Ex Machina: Personal Attacks Seen at Scale, Aggression and Friendliness

Are Chess Discussions Racist? An Adversarial Hate Speech Data Set

ETHOS: an Online Hate Speech Detection Dataset (Binary)

ETHOS: an Online Hate Speech Detection Dataset (Multi label)