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The dataset will be available on Kaggle and Hugging Face.

Kaggle#

You can see the dataset on OLID-BR | Kaggle.

The snippet below shows how to download the dataset using the Kaggle API.

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from kaggle.api.kaggle_api_extended import KaggleApi

kaggle = KaggleApi()
kaggle.authenticate()
kaggle.dataset_download_files(dataset="olidbr", unzip=True)

Hugging Face#

You can see the OLID-BR dataset on dougtrajano/olid-br · Datasets at Hugging Face.

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from datasets import load_dataset
dataset = load_dataset("dougtrajano/olid-br")

Dataset Files#

The dataset is composed of the following files:

  • train.csv: contains the training.
  • test.csv: contains the test data.
  • train_metadata.csv: contains the metadata of the training data.
  • test_metadata.csv: contains the metadata of the test data.
  • train.json: contains the training data in JSON format.
  • test.json: contains the test data in JSON format.
  • additional_data.json: contains additional data in JSON format. This data was not used in the creation of the dataset.

train.csv and test.csv follow the label assignment described in the Label Assignment section.

The JSON files (train.json, test.json, and additional_data.json) contain all three annotations and the metadata for each instance.

Hugging Face only has the train (train.csv) and test (test.csv) files.

Data Format#

CSV#

The CSV files are encoded in UTF-8 and have the following columns:

  • id (string): Unique identifier of the instance.
  • text (string): The text of the instance.
  • is_offensive (string): Whether the text is offensive (OFF) or not (NOT).
  • is_targeted (string): Whether the text is targeted (TIN) or untargeted (UNT).
  • targeted_type (string): Type of the target (individual IND, group GRP, or other OTH). Only available if is_targeted is True.
  • toxic_spans (string): List of toxic spans.
  • health (boolean): Whether the text contains hate speech based on health conditions such as disability, disease, etc.
  • ideology (boolean): Indicates if the text contains hate speech based on a person's ideas or beliefs.
  • insult (boolean): Whether the text contains insult, inflammatory, or provocative content.
  • lgbtqphobia (boolean): Whether the text contains harmful content related to gender identity or sexual orientation.
  • other_lifestyle (boolean): Whether the text contains hate speech related to life habits (e.g. veganism, vegetarianism, etc.).
  • physical_aspects (boolean): Whether the text contains hate speech related to physical appearance.
  • profanity_obscene (boolean): Whether the text contains profanity or obscene content.
  • racism (boolean): Whether the text contains prejudiced thoughts or discriminatory actions based on differences in race/ethnicity.
  • religious_intolerance (boolean): Whether the text contains religious intolerance.
  • sexism (boolean): Whether the text contains discriminatory content based on differences in sex/gender (e.g. sexism, misogyny, etc.).
  • xenophobia (boolean): Whether the text contains hate speech against foreigners.

The CSV files follow our label assignment strategy as described below.

  • is_offensive: majority vote.
  • is_targeted: majority vote.
  • targeted_type: majority vote.
  • toxic_spans: all labeled spans.
  • health: at least one.
  • ideology: at least one.
  • insult: at least one.
  • lgbtqphobia: at least one.
  • other_lifestyle: at least one.
  • physical_aspects: at least one.
  • profanity_obscene: at least one.
  • racism: at least one.
  • religious_intolerance: at least one.
  • sexism: at least one.
  • xenophobia: at least one.

JSON#

The JSON files are encoded in UTF-8 and have the following schema:

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{
  "id": "string",
  "text": "string",
  "metadata": {
    "source": "string",
    "created_at": "string",
    "collected_at": "string",
    "toxicity_score": "number",
  },
  "annotations": [
    {
      "annotator_id": "number",
      "is_offensive": "string",
      "is_targeted": "string",
      "targeted_type": "string",
      "toxic_spans": ["number"],
      "health": "boolean",
      "ideology": "boolean",
      "insult": "boolean",
      "lgbtqphobia": "boolean",
      "other_lifestyle": "boolean",
      "physical_aspects": "boolean",
      "profanity_obscene": "boolean",
      "racism": "boolean",
      "religious_intolerance": "boolean",
      "sexism": "boolean",
      "xenophobia": "boolean"
    }
  ]
}

Metadata#

We provide some metadata for the dataset to help further analysis.

Annotators#

For each qualified annotator, we asked him/her to provide the following information:

  • annotator_id: The annotator's unique ID.
  • age: The annotator's age.
  • gender The gender of the annotator.
    • Male
    • Female
    • Other
  • education_level: The education level of the annotator.
    • Primary School
    • Secondary School
    • Bachelor's Degree
    • Master's Degree
    • Doctoral Degree
  • annotator_type: The type of the annotator.
    • Volunteer
    • Researcher
    • Contract Worker
  • background: The background of the annotator.
    • Computer Science
    • Social Science

This information can be used to provide a better understanding of the annotator profile, maintaining the anonymity of the annotator

Comments#

For each comment, we collect contextual information based on the social media posts or the dataset that it comes from.

  • source The social media platform where the text was posted.
  • created_at The date and time of the post.
  • collected_at The date and time of the collection.
  • toxicity_score The toxicity score of the comment.
Last update: March 1, 2023