Dataset:
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
[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
```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.