Dataset:

Task Categories: text-classification
Languages: en
Multilinguality: monolingual
Size Categories: 10K<n<100K
Licenses: cc-by-4.0
Language Creators: crowdsourced
Annotations Creators: crowdsourced
Source Datasets: original

Dataset Card for hatexplain

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

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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

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

```bibtex @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 for adding this dataset.

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Models trained or fine-tuned on hatexplain

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