| ---
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| language:
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| - fra
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| license: cc-by-nc-sa-4.0
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| ---
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|
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| > [!NOTE]
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| > Dataset origin: https://github.com/adrianchifu/FreSaDa
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|
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| # FreSaDa: The **Fr**ench **Sa**tire **Da**ta Set
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| The FreSaDa data set contains regular and satirical samples of text collected from the French news domain.
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| ## Description
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| #### General Information
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| FreSaDa, the <i>**Fre**nch **Sa**tire **Da**ta Set</i>, is composed of 11,570 articles from the newsdomain.
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| The news articles are of two types: satirical and regular. Two possible tasks may be considered on FreSaDa:
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| - creoss-domain binary classification of full news articles into *regular* versus *satirical* examples
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| - creoss-domain binary classification of headlines into *regular* versus *satirical* examples
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| The data set is divided into three subsets:
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| - turaining (8,716 samples)
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| - telsting (2,854 samples)
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| Each sample contains the news article's title and text, as well as the corresponding label.
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| #### Data Organization
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| 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`:
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| The labels are associated as follows:
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| - 1 => Satiric News
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| - -1 => Regular News
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| If the experiments require a validation subset, the test subset may be divided into two equal parts:
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| - thae samples with odd rank (1st, 3rd, 5th, ...) for validation (1,427 samples)
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| - thae samples with even rank (2nd, 4th, 6th, ...) for testing (1,427 samples)
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| ## Citation
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| [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)
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| BibTeX citation:
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| ```BibTeX
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| @inproceedings{IonescuChifu2021IJCNN,
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| author = {Ionescu, Radu-Tudor and Chifu, Adrian-Gabriel},
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| title = {FreSaDa: A French Satire Data Set for Cross-Domain Satire Detection},
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| year = {2021},
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| booktitle = {The International Joint Conference on Neural Network, IJCNN 2021},
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| series = {IJCNN2021}
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| }
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| ``` |
