# files

_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: d1b8b9a8d309c8e29a64946b8767fbe78464b18c
file_path: content/datasets/detecting-abusive-albanian.md
extension: md
url_path: datasets/detecting-abusive-albanian
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{"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: 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: 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."}


