Datasets › Civil Comments

Civil Comments (Jigsaw Unintended Bias in Toxicity Classification)

Introduced by Daniel Borkan et al. in Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification11 Mar 2019 archive 2025-07-28

At the end of 2017 the Civil Comments platform shut down and chose make their ~2m public comments from their platform available in a lasting open archive so that researchers could understand and improve civility in online conversations for years to come. Jigsaw sponsored this effort and extended annotation of this data by human raters for various toxic conversational attributes.

In the data supplied for this competition, the text of the individual comment is found in the comment_text column. Each comment in Train has a toxicity label (target), and models should predict the target toxicity for the Test data. This attribute (and all others) are fractional values which represent the fraction of human raters who believed the attribute applied to the given comment.

The data also has several additional toxicity subtype attributes. Models do not need to predict these attributes for the competition, they are included as an additional avenue for research. Subtype attributes are:

  • severe_toxicity
  • obscene
  • threat
  • insult
  • identity_attack
  • sexual_explicit

Additionally, a subset of comments have been labelled with a variety of identity attributes, representing the identities that are mentioned in the comment. The columns corresponding to identity attributes are listed below. Only identities with more than 500 examples in the test set (combined public and private) will be included in the evaluation calculation. These identities are shown in bold.

  • male
  • female
  • transgender
  • other_gender
  • heterosexual
  • homosexual_gay_or_lesbian
  • bisexual
  • other_sexual_orientation
  • christian
  • jewish
  • muslim
  • hindu
  • buddhist
  • atheist
  • other_religion
  • black
  • white
  • asian
  • latino
  • other_race_or_ethnicity
  • physical_disability
  • intellectual_or_learning_disability
  • psychiatric_or_mental_illness
  • other_disability

Benchmarks archive 2025-07-28

All 1 leaderboard whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

First row (archive order)PaperCode
Toxic Comment Classification Civil Comments RoBERTa Focal Loss GMB BPSN 0.901 A benchmark for toxic comment classification on Civil... Nigiva/hatespeech-detection-models 22 Compare

Papers archive 2025-07-28

3 shown of 3 papers with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 156. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

DateSamples run Syntology
PyTorch Frame: A Modular Framework for Multi-Modal Tabular Learning 1 6 31 Mar 2024 ran 2 of 2 samples (0 unverified)
PaLM 2 Technical Report 1 2 17 May 2023 not harvested
A benchmark for toxic comment classification on Civil Comments dataset 1 14 26 Jan 2023 not harvested

Dataset loaders archive 2025-07-28

3 loaders as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

License archive 2025-07-28

Public domain (CC0)

Modalities archive 2025-07-28

Languages archive 2025-07-28

Variants archive 2025-07-28

  • Civil Comments

1 variant name, as the archive lists them.

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