# files

_id: 53e62d249aff0eafd8d8f249dd0fbb8f8b7dec4f
file_path: content/datasets/abusive-eval-v1-0.md
extension: md
url_path: datasets/abusive-eval-v1-0
metadata:
{"title":"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":14100,"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."}


_id: 2557c2fb1a97de8585ad9e8f3fd78f6964ffe957
file_path: content/datasets/abusive-language-detection-on-arabic-social-media-al-jazeera.md
extension: md
url_path: datasets/abusive-language-detection-on-arabic-social-media-al-jazeera
metadata:
{"title":"Abusive Language Detection on Arabic Social Media (Al Jazeera)","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":32000,"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."}


_id: 504f2913e825ff34c81b21f9bad56ddd3d5ad7f4
file_path: content/datasets/coral-a-context-aware-croatian-abusive-language-dataset.md
extension: md
url_path: datasets/coral-a-context-aware-croatian-abusive-language-dataset
metadata:
{"title":"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":2240,"percentage-abusive":100,"language":"Croatian","level-of-annotation":["Posts"],"platform":["Posts"],"medium":["Newspaper Comments"],"reference":"Ravi Shekhar, Mladen Karan and Matthew Purver (2022). CoRAL: a Context-aware Croatian Abusive Language Dataset. Findings of the ACL: AACL-IJCNLP."}


_id: d1b8b9a8d309c8e29a64946b8767fbe78464b18c
file_path: content/datasets/detecting-abusive-albanian.md
extension: md
url_path: datasets/detecting-abusive-albanian
metadata:
{"title":"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":11874,"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"}


_id: eee4dc70a2824ff37f0d4cf01d38b1bff3a13a2b
extension: md
url_path: datasets/hate-speech-detection-in-the-bengali-language-a-dataset-and-its-baseline-evaluation
file_path:
content/datasets/hate-speech-detection-in-the-bengali-language-a-dataset-and-its-baseline-evaluation.md

metadata:
{"title":"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":30000,"percentage-abusive":0.33,"language":"Bengali","level-of-annotation":["Posts"],"platform":["Youtube","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."}


_id: 4d6036a7abf536acda3749af57dceff05a57c20e
file_path: content/datasets/large-scale-hate-speech-detection-with-cross-domain-tranfer.md
extension: md
url_path: datasets/large-scale-hate-speech-detection-with-cross-domain-tranfer
metadata:
{"title":"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","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."}


_id: 9f1925dc3eedef62ff803fda71aa9f36921766de
file_path: content/datasets/let-mi-an-arabic-levantine-twitter-dataset-for-mysogynistic-language.md
extension: md
url_path: datasets/let-mi-an-arabic-levantine-twitter-dataset-for-mysogynistic-language
metadata:
{"title":"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":6603,"percentage-abusive":48.76,"language":"Arabic","level-of-annotation":["Posts"],"platform":["Twitter"],"medium":["Text","Images"],"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"}


_id: f7df9a5dfe65080326c3ce11c8f60d8a55cc8981
file_path: content/datasets/measuring-hate-speech.md
extension: md
url_path: datasets/measuring-hate-speech
metadata:
{"title":"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,"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."}


_id: ba4fbb5b0ed39a3f654646b1c29d57e87fde08aa
file_path: content/datasets/offensive-language-and-hate-speech-detection-for-danish.md
extension: md
url_path: datasets/offensive-language-and-hate-speech-detection-for-danish
metadata:
{"title":"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":3600,"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."}


_id: 4766272b53d1f2887671cc05ecc982f1e589a201
file_path: content/keywords/hurtlex.md
extension: md
url_path: keywords/hurtlex
metadata:
{"title":"Hurtlex","description":"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":"https://github.com/valeriobasile/hurtlex","reference":"http://ceur-ws.org/Vol-2253/paper49.pdf, Proc. CLiC-it 2018"}


_id: 9d7b5d0e8a5319da4d31e34915d7604da15fa242
file_path: content/keywords/jiang-et-al.md
extension: md
url_path: keywords/jiang-et-al
metadata:
{"title":"SexHateLex is a Chinese lexicon of hateful and sexist words.","data-link":"https://doi.org/10.5281/zenodo.4773875","reference":"http://ceur-ws.org/Vol-2253/paper49.pdf, Journal of OSNEM, Vol.27, 2022, 100182, ISSN 2468-6964."}


