Datasets:

Silly-Machine
/

TuPyE-Dataset

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-Dataset
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
  - config_name: binary
    data_files:
      - split: train
        path: binary/binary_train.csv
      - split: test
        path: binary/binary_test.csv

Portuguese Hate Speech Expanded Dataset (TuPyE)

TuPyE, an enhanced iteration of TuPy, encompasses a compilation of 43,668 meticulously annotated documents specifically selected for the purpose of hate speech detection within diverse social network contexts. This augmented dataset integrates supplementary annotations and amalgamates with datasets sourced from Fortuna et al. (2019), Leite et al. (2020), and Vargas et al. (2022), complemented by an infusion of 10,000 original documents from the TuPy-Dataset.

The amalgamation of these new annotations and the seamless integration with the aforementioned cited datasets significantly amplifies the efficacy of TuPyE. This augmentation facilitates the development of sophisticated hate speech detection models, leveraging Machine Learning (ML) and Natural Language Processing (NLP) techniques. This repository is organized as follows:

root.
    ├── binary     : binary dataset (including training and testing split)
    ├── multilabel : multilabel dataset (including training and testing split)
    └── README.md  : documentation and card metadata

Security measures

To safeguard user identity and uphold the integrity of this dataset, all user mentions have been anonymized as "@user," and any references to external websites have been omitted

Annotation and voting process

Regarding the unpublished part of the TuPyE dataset, we utilized a simple voting process to generate the binary matrices. Each document underwent three separate evaluations. If a document received two or more identical classifications, the assigned value was set to 1; otherwise, it was marked as 0. The annotated raw data can be accessed in the project repository. The following table offers a brief summary of the annotators' profiles and qualifications:

Table 1 – Annotators

Annotator	Gender	Education	Political	Color
Annotator 1	Female	Ph.D. Candidate in civil engineering	Far-left	White
Annotator 2	Male	Master's candidate in human rights	Far-left	Black
Annotator 3	Female	Master's degree in behavioral psychology	Liberal	White
Annotator 4	Male	Master's degree in behavioral psychology	Right-wing	Black
Annotator 5	Female	Ph.D. Candidate in behavioral psychology	Liberal	Black
Annotator 6	Male	Ph.D. Candidate in linguistics	Far-left	White
Annotator 7	Female	Ph.D. Candidate in civil engineering	Liberal	White
Annotator 8	Male	Ph.D. Candidate in civil engineering	Liberal	Black
Annotator 9	Male	Master's degree in behavioral psychology	Far-left	White

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 TuPy dataset is depicted below:

{
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
}

Dataset content

Table 2 provides a detailed breakdown of the dataset, delineating the volume of data based on the occurrence of aggressive speech and the manifestation of hate speech within the documents

Table 2 - Count of non-aggressive and aggressive documents

Label	Count
Non-aggressive	31121
Aggressive - Not hate	3180
Aggressive - Hate	9367
Total	43668

Table 3 provides a detailed analysis of the dataset, delineating the data volume in relation to the occurrence of distinct categories of hate speech.

Table 3 - Hate categories count

Label	Count
Ageism	57
Aporophobia	66
Body shame	285
Capacitism	99
LGBTphobia	805
Political	1149
Racism	290
Religious intolerance	108
Misogyny	1675
Xenophobia	357
Other	4476
Total	9367

BibTeX citation

This dataset can be cited as follows:

@misc {silly-machine_2023,
    author       = { {Silly-Machine} },
    title        = { TuPy-Dataset (Revision de6b18c) },
    year         = 2023,
    url          = { https://huggingface.co/datasets/Silly-Machine/TuPy-Dataset },
    doi          = { 10.57967/hf/1529 },
    publisher    = { Hugging Face }
}

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) and the Alberto Luiz Coimbra Institute for Postgraduate Studies and Research in Engineering (COPPE).