Dataset: civil_comments


Dataset Card for "civil_comments"

Table of Contents

Dataset Description

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

More Information Needed

Languages

More Information Needed

Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

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 Sample Size

name train validation test
default 1804874 97320 97320

Dataset Creation

Curation Rationale

More Information Needed

Source Data

More Information Needed

Annotations

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}
}

Models trained or fine-tuned on civil_comments

None yet