task_categories:text-classification language_creators:found multilinguality:monolingual size_categories:n<1K language:pt license:mit region:us
\
@inproceedings{10.1145/3428658.3430965,
  author = {da Silva, Fl\'{a}vio Roberto Matias and Freire, Paulo M\'{a}rcio Souza and de Souza, Marcelo Pereira and de A. B. Plenamente, Gustavo and Goldschmidt, Ronaldo Ribeiro},
  title = {FakeNewsSetGen: A Process to Build Datasets That Support Comparison Among Fake News Detection Methods},
  year = {2020},
  isbn = {9781450381963},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3428658.3430965},
  doi = {10.1145/3428658.3430965},
  abstract = {Due to easy access and low cost, social media online news consumption has increased significantly for the last decade. Despite their benefits, some social media allow anyone to post news with intense spreading power, which amplifies an old problem: the dissemination of Fake News. In the face of this scenario, several machine learning-based methods to automatically detect Fake News (MLFN) have been proposed. All of them require datasets to train and evaluate their detection models. Although recent MLFN were designed to consider data regarding the news propagation on social media, most of the few available datasets do not contain this kind of data. Hence, comparing the performances amid those recent MLFN and the others is restricted to a very limited number of datasets. Moreover, all existing datasets with propagation data do not contain news in Portuguese, which impairs the evaluation of the MLFN in this language. Thus, this work proposes FakeNewsSetGen, a process that builds Fake News datasets that contain news propagation data and support comparison amid the state-of-the-art MLFN. FakeNewsSetGen's software engineering process was guided to include all kind of data required by the existing MLFN. In order to illustrate FakeNewsSetGen's viability and adequacy, a case study was carried out. It encompassed the implementation of a FakeNewsSetGen prototype and the application of this prototype to create a dataset called FakeNewsSet, with news in Portuguese. Five MLFN with different kind of data requirements (two of them demanding news propagation data) were applied to FakeNewsSet and compared, demonstrating the potential use of both the proposed process and the created dataset.},
  booktitle = {Proceedings of the Brazilian Symposium on Multimedia and the Web},
  pages = {241–248},
  numpages = {8},
  keywords = {Fake News detection, Dataset building process, social media},
  location = {S\~{a}o Lu\'{\i}s, Brazil},
  series = {WebMedia '20}
}
---
license: mit
task_categories:
- text-classification
language:
- pt
size_categories:
- n<1K
language_details: pt-BR
multilinguality:
  - monolingual
language_creators:
  - found
---
# FakeNewsSet

## Dataset Description

- **Homepage:** 
- **Repository:** [https://dl.acm.org/doi/abs/10.1145/3428658.3430965](https://dl.acm.org/doi/abs/10.1145/3428658.3430965)
- **Paper:** [https://dl.acm.org/doi/abs/10.1145/3428658.3430965](https://dl.acm.org/doi/abs/10.1145/3428658.3430965)
- **Leaderboard:** 
- **Point of Contact:** 

### Dataset Summary


### 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 "FakeNewsSet", please cite:

```bibtex
@inproceedings{10.1145/3428658.3430965,
  author = {da Silva, Fl\'{a}vio Roberto Matias and Freire, Paulo M\'{a}rcio Souza and de Souza, Marcelo Pereira and de A. B. Plenamente, Gustavo and Goldschmidt, Ronaldo Ribeiro},
  title = {FakeNewsSetGen: A Process to Build Datasets That Support Comparison Among Fake News Detection Methods},
  year = {2020},
  isbn = {9781450381963},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/3428658.3430965},
  doi = {10.1145/3428658.3430965},
  abstract = {Due to easy access and low cost, social media online news consumption has increased significantly for the last decade. Despite their benefits, some social media allow anyone to post news with intense spreading power, which amplifies an old problem: the dissemination of Fake News. In the face of this scenario, several machine learning-based methods to automatically detect Fake News (MLFN) have been proposed. All of them require datasets to train and evaluate their detection models. Although recent MLFN were designed to consider data regarding the news propagation on social media, most of the few available datasets do not contain this kind of data. Hence, comparing the performances amid those recent MLFN and the others is restricted to a very limited number of datasets. Moreover, all existing datasets with propagation data do not contain news in Portuguese, which impairs the evaluation of the MLFN in this language. Thus, this work proposes FakeNewsSetGen, a process that builds Fake News datasets that contain news propagation data and support comparison amid the state-of-the-art MLFN. FakeNewsSetGen's software engineering process was guided to include all kind of data required by the existing MLFN. In order to illustrate FakeNewsSetGen's viability and adequacy, a case study was carried out. It encompassed the implementation of a FakeNewsSetGen prototype and the application of this prototype to create a dataset called FakeNewsSet, with news in Portuguese. Five MLFN with different kind of data requirements (two of them demanding news propagation data) were applied to FakeNewsSet and compared, demonstrating the potential use of both the proposed process and the created dataset.},
  booktitle = {Proceedings of the Brazilian Symposium on Multimedia and the Web},
  pages = {241–248},
  numpages = {8},
  keywords = {Fake News detection, Dataset building process, social media},
  location = {S\~{a}o Lu\'{\i}s, Brazil},
  series = {WebMedia '20}
}
```

### Contributions

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