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

![](https://dougtrajano.github.io/olid-br/images/olid-br-taxonomy.png)

## Categorization

### Offensive Content Detection

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

**Is this text offensive?**

We use the [Perspective API](https://www.perspectiveapi.com/) to detect if the sentence contains offensive content with double-checking by our [qualified annotators](annotation/index.en.md#who-are-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**](glossary.en.md) 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:

```python
[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**](get-started.en.md) 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