metadata

language: pt
license: cc-by-4.0
dataset_info:
  features:
    - name: id
      dtype: string
    - name: text
      dtype: string
    - name: is_offensive
      dtype: string
    - name: is_targeted
      dtype: string
    - name: targeted_type
      dtype: string
    - name: toxic_spans
      sequence: int64
    - name: health
      dtype: bool
    - name: ideology
      dtype: bool
    - name: insult
      dtype: bool
    - name: lgbtqphobia
      dtype: bool
    - name: other_lifestyle
      dtype: bool
    - name: physical_aspects
      dtype: bool
    - name: profanity_obscene
      dtype: bool
    - name: racism
      dtype: bool
    - name: religious_intolerance
      dtype: bool
    - name: sexism
      dtype: bool
    - name: xenophobia
      dtype: bool
  splits:
    - name: train
      num_bytes: 1763684
      num_examples: 5214
    - name: test
      num_bytes: 590953
      num_examples: 1738
  download_size: 1011742
  dataset_size: 2354637

OLID-BR

Offensive Language Identification Dataset for Brazilian Portuguese (OLID-BR) is a dataset with multi-task annotations for the detection of offensive language.

The current version (v1.0) contains 7,943 (extendable to 13,538) comments from different sources, including social media (YouTube and Twitter) and related datasets.

OLID-BR contains a collection of annotated sentences in Brazilian Portuguese using an annotation model that encompasses the following levels:

Offensive content detection: Detect offensive content in sentences and categorize it.
Offense target identification: Detect if an offensive sentence is targeted to a person or group of people.
Offensive spans identification: Detect curse words in sentences.

Categorization

Offensive Content Detection

This level is used to detect offensive content in the sentence.

Is this text offensive?

We use the Perspective API to detect if the sentence contains offensive content with double-checking by our qualified annotators.

OFF Offensive: Inappropriate language, insults, or threats.
NOT Not offensive: No offense or profanity.

Which kind of offense does it contain?

The following labels were tagged by our annotators:

Health, Ideology, Insult, LGBTQphobia, Other-Lifestyle, Physical Aspects, Profanity/Obscene, Racism, Religious Intolerance, Sexism, and Xenophobia.

See the Glossary for further information.

Offense Target Identification

This level is used to detect if an offensive sentence is targeted to a person or group of people.

Is the offensive text targeted?

TIN Targeted Insult: Targeted insult or threat towards an individual, a group or other.
UNT Untargeted: Non-targeted profanity and swearing.

What is the target of the offense?

IND The offense targets an individual, often defined as “cyberbullying”.
GRP The offense targets a group of people based on ethnicity, gender, sexual
OTH The target can belong to other categories, such as an organization, an event, an issue, etc.

Offensive Spans Identification

As toxic spans, we define a sequence of words that attribute to the text's toxicity.

For example, let's consider the following text:

"USER Canalha URL"

The toxic spans are:

[5, 6, 7, 8, 9, 10, 11, 12, 13]

Dataset Structure

Data Instances

Each instance is a social media comment with a corresponding ID and annotations for all the tasks described below.

Data Fields

The simplified configuration includes:

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.

See the Get Started page for more information.

Considerations for Using the Data

Social Impact of Dataset

Toxicity detection is a worthwhile problem that can ensure a safer online environment for everyone.

However, toxicity detection algorithms have focused on English and do not consider the specificities of other languages.

This is a problem because the toxicity of a comment can be different in different languages.

Additionally, the toxicity detection algorithms focus on the binary classification of a comment as toxic or not toxic.

Therefore, we believe that the OLID-BR dataset can help to improve the performance of toxicity detection algorithms in Brazilian Portuguese.

Discussion of Biases

We are aware that the dataset contains biases and is not representative of global diversity.

We are aware that the language used in the dataset could not represent the language used in different contexts.

Potential biases in the data include: Inherent biases in the social media and user base biases, the offensive/vulgar word lists used for data filtering, and inherent or unconscious bias in the assessment of offensive identity labels.

All these likely affect labeling, precision, and recall for a trained model.

Citation

Pending