metadata

annotations_creators:
  - crowdsourced
  - expert-generated
language_creators:
  - found
language:
  - ko
license:
  - cc-by-sa-4.0
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - multi-label-classification
paperswithcode_id: korean-hatespeech-dataset
pretty_name: Korean HateSpeech Dataset
dataset_info:
  features:
    - name: comments
      dtype: string
    - name: contain_gender_bias
      dtype:
        class_label:
          names:
            '0': 'False'
            '1': 'True'
    - name: bias
      dtype:
        class_label:
          names:
            '0': none
            '1': gender
            '2': others
    - name: hate
      dtype:
        class_label:
          names:
            '0': hate
            '1': offensive
            '2': none
  splits:
    - name: train
      num_bytes: 983608
      num_examples: 7896
    - name: test
      num_bytes: 58913
      num_examples: 471
  download_size: 968449
  dataset_size: 1042521

Dataset Card for [Dataset Name]

Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage: Korean HateSpeech Dataset
Repository: Korean HateSpeech Dataset
Paper: BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection
Point of Contact: Steven Liu

Dataset Summary

The Korean HateSpeech Dataset is a dataset of 8367 human-labeled entertainment news comments from a popular Korean news aggregation platform. Each comment was evaluated for either social bias (labels: gender, others none), hate speech (labels: hate, offensive, none) or gender bias (labels: True, False). The dataset was created to support the identification of toxic comments on online platforms where users can remain anonymous.

Supported Tasks and Leaderboards

multi-label classification: The dataset can be used to train a model for hate speech detection. A BERT model can be presented with a Korean entertainment news comment and be asked to label whether it contains social bias, gender bias and hate speech. Users can participate in a Kaggle leaderboard here.

Languages

The text in the dataset is in Korean and the associated is BCP-47 code is ko-KR.

Dataset Structure

Data Instances

An example data instance contains a comments containing the text of the news comment and then labels for each of the following fields: contain_gender_bias, bias and hate.

{'comments':'설마 ㅈ 현정 작가 아니지??'
 'contain_gender_bias': 'True',
 'bias': 'gender',
 'hate': 'hate'
}

Data Fields

comments: text from the Korean news comment
contain_gender_bias: a binary True/False label for the presence of gender bias
bias: determines the type of social bias, which can be:
- gender: if the text includes bias for gender role, sexual orientation, sexual identity, and any thoughts on gender-related acts
- others: other kinds of factors that are considered not gender-related but social bias, including race, background, nationality, ethnic group, political stance, skin color, religion, handicaps, age, appearance, richness, occupations, the absence of military service experience
- none: a comment that does not incorporate the bias
hate: determines how aggressive the comment is, which can be:
- hate: if the text is defined as an expression that display aggressive stances towards individuals/groups with certain characteristics (gender role, sexual orientation, sexual identity, any thoughts on gender-related acts, race, background, nationality, ethnic group, political stance, skin color, religion, handicaps, age, appearance, richness, occupations, the absence of military service experience, etc.)
- offensive: if the text contains rude or aggressive contents, can emit sarcasm through rhetorical question or irony, encompass an unethical expression or conveys unidentified rumors
- none: a comment that does not incorporate hate

Data Splits

The data is split into a training and development (test) set. It contains 8371 annotated comments that are split into 7896 comments in the training set and 471 comments in the test set.

Dataset Creation

Curation Rationale

The dataset was created to provide the first human-labeled Korean corpus for toxic speech detection from a Korean online entertainment news aggregator. Recently, two young Korean celebrities suffered from a series of tragic incidents that led to two major Korean web portals to close the comments section on their platform. However, this only serves as a temporary solution, and the fundamental issue has not been solved yet. This dataset hopes to improve Korean hate speech detection.

Source Data

Initial Data Collection and Normalization

A total of 10.4 million comments were collected from an online Korean entertainment news aggregator between Jan. 1, 2018 and Feb. 29, 2020. 1,580 articles were drawn using stratified sampling and the top 20 comments were extracted ranked in order of their Wilson score on the downvote for each article. Duplicate comments, single token comments and comments with more than 100 characters were removed (because they could convey various opinions). From here, 10K comments were randomly chosen for annotation.

Who are the source language producers?

The language producers are users of the Korean online news platform between 2018 and 2020.

Annotations

Annotation process

Each comment was assigned to three random annotators to assign a majority decision. For more ambiguous comments, annotators were allowed to skip the comment. See Appendix A in the paper for more detailed guidelines.

Who are the annotators?

Annotation was performed by 32 annotators, consisting of 29 annotators from the crowdsourcing platform DeepNatural AI and three NLP researchers.

Personal and Sensitive Information

[N/A]

Considerations for Using the Data

Social Impact of Dataset

The purpose of this dataset is to tackle the social issue of users creating toxic comments on online platforms. This dataset aims to improve detection of toxic comments online.

Discussion of Biases

[More Information Needed]

Other Known Limitations