hate_speech
korean-hate-speechreference: https://github.com/kocohub/korean-hate-speech
@inproceedings{moon-etal-2020-beep,
title = "{BEEP}! {K}orean Corpus of Online News Comments for Toxic Speech Detection",
author = "Moon, Jihyung and
Cho, Won Ik and
Lee, Junbum",
booktitle = "Proceedings of the Eighth International Workshop on Natural Language Processing for Social Media",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics"… See the full description on the dataset page: https://huggingface.co/datasets/nayohan/korean-hate-speech.hate_speech_offensive
Dataset Card for [Dataset Name]
Dataset Summary
An annotated dataset for hate speech and offensive language detection on tweets.
Supported Tasks and Leaderboards
[More Information Needed]
Languages
English (en)
Dataset Structure
Data Instances
{
"count": 3,
"hate_speech_annotation": 0,
"offensive_language_annotation": 0,
"neither_annotation": 3,
"label": 2, # "neither"
"tweet": "!!! RT @mayasolovely: As a woman you… See the full description on the dataset page: https://huggingface.co/datasets/tdavidson/hate_speech_offensive.measuring-hate-speech
Dataset card for Measuring Hate Speech
This is a public release of the dataset described in Kennedy et al. (2020) and Sachdeva et al. (2022), consisting of 39,565 comments annotated by 7,912 annotators, for 135,556 combined rows. The primary outcome variable is the "hate speech score" but the 10 constituent ordinal labels (sentiment, (dis)respect, insult, humiliation, inferior status, violence, dehumanization, genocide, attack/defense, hate speech benchmark) can also be treated as… See the full description on the dataset page: https://huggingface.co/datasets/ucberkeley-dlab/measuring-hate-speech.kmhas_korean_hate_speechThe K-MHaS (Korean Multi-label Hate Speech) dataset contains 109k utterances from Korean online news comments labeled with 8 fine-grained hate speech classes or Not Hate Speech class.
The fine-grained hate speech classes are politics, origin, physical, age, gender, religion, race, and profanity and these categories are selected in order to reflect the social and historical context.hatexplainHatexplain is the first benchmark hate speech dataset covering multiple aspects of the issue. Each post in the dataset is annotated from three different perspectives: the basic, commonly used 3-class classification (i.e., hate, offensive or normal), the target community (i.e., the community that has been the victim of hate speech/offensive speech in the post), and the rationales, i.e., the portions of the post on which their labelling decision (as hate, offensive or normal) is based.korean-hate-speechHello AI-it!