---
language:
- fr
license: cc-by-nc-sa-4.0
---

> [!NOTE]
> Dataset origin: https://github.com/adrianchifu/FreSaDa

# FreSaDa: The **Fr**ench **Sa**tire **Da**ta Set

The FreSaDa data set contains regular and satirical samples of text collected from the French news domain.


## Description

#### General Information

FreSaDa, the <i>**Fre**nch **Sa**tire **Da**ta Set</i>, is composed of 11,570  articles  from  the  newsdomain. 
The news articles are of two types: satirical and regular. Two possible tasks may be considered on FreSaDa:
- Cross-domain binary classification of full news articles into *regular* versus *satirical* examples
- Cross-domain binary classification of headlines into *regular* versus *satirical* examples

The data set is divided into three subsets:
- training (8,716 samples)
- testing (2,854 samples)

Each sample contains the news article's title and text, as well as the corresponding label.

#### Data Organization

The data set is divided in two folders, `train` and `test`, corresponding to the two subsets for training and testing. In each folder there is a subfolder entitled `texts`, containing the texts of the news articles. Each folder also contains a file called `summary.tsv`:

  The labels are associated as follows:
  -  1 => Satiric News
  - -1 => Regular News
  
  If the experiments require a validation subset, the test subset may be divided into two equal parts: 
  - the samples with odd rank (1st, 3rd, 5th, ...) for validation (1,427 samples)
  - the samples with even rank (2nd, 4th, 6th, ...) for testing (1,427 samples)


## Citation

[1] *Radu Tudor Ionescu, Adrian Gabriel Chifu.* **FreSaDa: A French Satire Data Set for Cross-Domain Satire Detection.** In: The International Joint Conference on Neural Network, IJCNN 2021 (2021). [(link to article)](https://arxiv.org/abs/2104.04828)

BibTeX citation:
```BibTeX
@inproceedings{IonescuChifu2021IJCNN,
author = {Ionescu, Radu-Tudor and Chifu, Adrian-Gabriel},
title = {FreSaDa: A French Satire Data Set for Cross-Domain Satire Detection},
year = {2021},
booktitle = {The International Joint Conference on Neural Network, IJCNN 2021},
series = {IJCNN2021}
}
```