Datasets:
Tasks:
Text Classification
Formats:
csv
Languages:
Portuguese
Size:
10K - 100K
ArXiv:
Tags:
hate-speech-detection
DOI:
License:
File size: 2,928 Bytes
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---
license: cc-by-4.0
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- pt
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
pretty_name: TuPy
language_bcp47:
- pt-BR
tags:
- hate-speech-detection
configs:
- config_name: multilabel
data_files:
- split: train
path: multilabel/multilabel_train.csv
- split: test
path: multilabel/multilabel_test.csv
default: true
- config_name: binary
data_files:
- split: train
path: binary/binary_train.csv
- split: test
path: binary/binary_test.csv
---
# Portuguese Hate Speech Dataset (TuPy)
The Portuguese hate speech dataset (TuPy) is an annotated corpus designed to facilitate the development of advanced hate speech detection models using machine learning (ML) and natural language processing (NLP) techniques. TuPy is formed by 10000 thousand unpublished, annotated, and anonymous tweets collected in 2023.
This repository is organized as follows:
```sh
root.
├── binary : binary dataset (including training and testing split)
├── multilabel : multilabel dataset (including training and testing split)
└── README.md : documentation and card metadata
```
## Voting process
To generate the binary matrices, we employed a straightforward voting process. Three distinct evaluations were assigned to each document. In cases where a document received two or more identical classifications, the adopted value is set to 1; otherwise, it is marked as 0.Raw data can be checked into the repository in the [project repository](https://github.com/Silly-Machine/TuPy-Dataset)
## Data structure
A data point comprises the tweet text (a string) along with thirteen categories, each category is assigned a value of 0 when there is an absence of aggressive or hateful content and a value of 1 when such content is present. These values represent the consensus of annotators regarding the presence of aggressive, hate, ageism, aporophobia, body shame, capacitism, lgbtphobia, political, racism, religious intolerance, misogyny, xenophobia, and others. An illustration from the multilabel ToLD-Br dataset is depicted below:
```python
{text: "e tem pobre de direita imbecil que ainda defendia a manutenção da política de preços atrelada ao dólar link",
aggressive: 1,
hate: 1,
ageism: 0,
aporophobia: 1,
body shame: 0,
capacitism: 0,
lgbtphobia: 0,
political: 1
racism : 0,
religious intolerance : 0,
misogyny : 0,
xenophobia : 0,
other : 0}
```
## Acknowledge
The TuPy project is the result of the development of Felipe Oliveira's thesis and the work of several collaborators. This project is financed by the Federal University of Rio de Janeiro ([UFRJ](https://ufrj.br/)) and the Alberto Luiz Coimbra Institute for Postgraduate Studies and Research in Engineering ([COPPE](https://coppe.ufrj.br/)). |