language_creators:found multilinguality:monolingual size_categories:10K<n<100K language:pt region:us
---
language:
- pt
language_details: pt-BR
pretty_name: FakeRecogna
size_categories:
- 10K<n<100K
multilinguality:
- monolingual
language_creators:
- found
---
# FakeRecogna

## Dataset Description

- **Homepage:** [https://github.com/Gabriel-Lino-Garcia/FakeRecogna](https://github.com/Gabriel-Lino-Garcia/FakeRecogna)
- **Repository:** 
- **Paper:** 
- **Leaderboard:** 
- **Point of Contact:** 

### Dataset Summary

FakeRecogna is a dataset comprised of real and fake news. 
The real news is not directly linked to fake news and vice-versa, which could lead to a biased classification.
The news collection was performed by crawlers developed for mining pages of well-known and of great national importance agency news.

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

The dataset is in Portuguese.

## Dataset Structure

### Data Instances

[More Information Needed]

### Data Fields

[More Information Needed]

### Data Splits

[More Information Needed]

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

If you use "FakeRecogna Dataset", please cite:

```bibtex
@inproceedings{10.1007/978-3-030-98305-5_6,
author = {Garcia, Gabriel L. and Afonso, Luis C. S. and Papa, Jo\~{a}o P.},
title = {FakeRecogna: A New Brazilian Corpus for Fake News Detection},
year = {2022},
isbn = {978-3-030-98304-8},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-030-98305-5_6},
doi = {10.1007/978-3-030-98305-5_6},
abstract = {Fake news has become a research topic of great importance in Natural Language Processing due to its negative impact on our society. Although its pertinence, there are few datasets available in Brazilian Portuguese and mostly comprise few samples. Therefore, this paper proposes creating a new fake news dataset named FakeRecogna that contains a greater number of samples, more up-to-date news, and covering a few of the most important categories. We perform a toy evaluation over the created dataset using traditional classifiers such as Naive Bayes, Optimum-Path Forest, and Support Vector Machines. A Convolutional Neural Network is also evaluated in the context of fake news detection in the proposed dataset.},
booktitle = {Computational Processing of the Portuguese Language: 15th International Conference, PROPOR 2022, Fortaleza, Brazil, March 21–23, 2022, Proceedings},
pages = {57–67},
numpages = {11},
keywords = {Fake news, Corpus, Portuguese},
location = {Fortaleza, Brazil}
}
```

### Contributions

Thanks to [@ju-resplande](https://github.com/ju-resplande) for adding this dataset.