README.md

metadata

annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
languages:
  - en
licenses:
  - cc-by-4-0
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - text-classification-other-hate-speech-detection

Dataset Card for hatexplain

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: [Needs More Information]
• Repository: https://github.com/punyajoy/HateXplain/
• Paper: https://arxiv.org/abs/2012.10289
• Leaderboard: [Needs More Information]
• Point of Contact: [Needs More Information]

Dataset Summary

Hatexplain 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 labeling decision (as hate, offensive or normal) is based.

WARNING: This dataset contains content that are offensive and/or hateful in nature.

Supported Tasks and Leaderboards

[Needs More Information]

Languages

The language supported is English.

Dataset Structure

Data Instances

Sample Entry:

{
    "id": "24198545_gab",
    "annotators": [
      {
        "label": 0,  # hatespeech
        "annotator_id": 4,
        "target": ["African"]
      },
      {
        "label": 0,  # hatespeech
        "annotator_id": 3,
        "target": ["African"]
      },
      {
        "label": 2,  # offensive
        "annotator_id": 5,
        "target": ["African"]
      }
    ],
    "rationales":[
    [0,0,0,0,0,0,0,0,1,0,0,1,1,1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0],
    [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
    [0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
    ],
    "post_tokens": ["and","this","is","why","i","end","up","with","nigger","trainee","doctors","who","can","not","speak","properly","lack","basic","knowledge","of","biology","it","truly","scary","if","the","public","only","knew"]
  }
}

Data Fields

:small_blue_diamond:post_id : Unique id for each post
:small_blue_diamond:annotators : The list of annotations from each annotator
:small_blue_diamond:annotators[label] : The label assigned by the annotator to this post. Possible values: hatespeech (0), normal (1) or offensive (2)
:small_blue_diamond:annotators[annotator_id] : The unique Id assigned to each annotator
:small_blue_diamond:annotators[target] : A list of target community present in the post
:small_blue_diamond:rationales : A list of rationales selected by annotators. Each rationales represents a list with values 0 or 1. A value of 1 means that the token is part of the rationale selected by the annotator. To get the particular token, we can use the same index position in "post_tokens"
:small_blue_diamond:post_tokens : The list of tokens representing the post which was annotated

Data Splits

Post_id_divisions has a dictionary having train, valid and test post ids that are used to divide the dataset into train, val and test set in the ratio of 8:1:1.

Dataset Creation

Curation Rationale

[Needs More Information]

Source Data

Initial Data Collection and Normalization

[Needs More Information]

Who are the source language producers?

[Needs More Information]

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

[Needs More Information]

Considerations for Using the Data

Social Impact of Dataset

[Needs More Information]

Discussion of Biases

[Needs More Information]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

[Needs More Information]

Licensing Information

[Needs More Information]

Citation Information

@misc{mathew2020hatexplain,
      title={HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection}, 
      author={Binny Mathew and Punyajoy Saha and Seid Muhie Yimam and Chris Biemann and Pawan Goyal and Animesh Mukherjee},
      year={2020},
      eprint={2012.10289},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

### Contributions

Thanks to [@kushal2000](https://github.com/kushal2000) for adding this dataset.