README.md

metadata

languages:
  - en
paperswithcode_id: null
pretty_name: CivilComments

Dataset Card for "civil_comments"

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • 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: https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data
• Repository: More Information Needed
• Paper: More Information Needed
• Point of Contact: More Information Needed
• Size of downloaded dataset files: 395.73 MB
• Size of the generated dataset: 630.60 MB
• Total amount of disk used: 1026.33 MB

Dataset Summary

The comments in this dataset come from an archive of the Civil Comments platform, a commenting plugin for independent news sites. These public comments were created from 2015 - 2017 and appeared on approximately 50 English-language news sites across the world. When Civil Comments shut down in 2017, they chose to make the public comments available in a lasting open archive to enable future research. The original data, published on figshare, includes the public comment text, some associated metadata such as article IDs, timestamps and commenter-generated "civility" labels, but does not include user ids. Jigsaw extended this dataset by adding additional labels for toxicity and identity mentions. This data set is an exact replica of the data released for the Jigsaw Unintended Bias in Toxicity Classification Kaggle challenge. This dataset is released under CC0, as is the underlying comment text.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

default

• Size of downloaded dataset files: 395.73 MB
• Size of the generated dataset: 630.60 MB
• Total amount of disk used: 1026.33 MB

An example of 'validation' looks as follows.

{
    "identity_attack": 0.0,
    "insult": 0.0,
    "obscene": 0.0,
    "severe_toxicity": 0.0,
    "sexual_explicit": 0.0,
    "text": "The public test.",
    "threat": 0.0,
    "toxicity": 0.0
}

Data Fields

The data fields are the same among all splits.

default

• text: a string feature.
• toxicity: a float32 feature.
• severe_toxicity: a float32 feature.
• obscene: a float32 feature.
• threat: a float32 feature.
• insult: a float32 feature.
• identity_attack: a float32 feature.
• sexual_explicit: a float32 feature.

Data Splits

name	train	validation	test
default	1804874	97320	97320

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

More Information Needed

Citation Information

@article{DBLP:journals/corr/abs-1903-04561,
  author    = {Daniel Borkan and
               Lucas Dixon and
               Jeffrey Sorensen and
               Nithum Thain and
               Lucy Vasserman},
  title     = {Nuanced Metrics for Measuring Unintended Bias with Real Data for Text
               Classification},
  journal   = {CoRR},
  volume    = {abs/1903.04561},
  year      = {2019},
  url       = {http://arxiv.org/abs/1903.04561},
  archivePrefix = {arXiv},
  eprint    = {1903.04561},
  timestamp = {Sun, 31 Mar 2019 19:01:24 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/abs-1903-04561},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Contributions

Thanks to @lewtun, @patrickvonplaten, @thomwolf for adding this dataset.