---
dataset_info:
  features:
  - name: index
    dtype: int64
  - name: news_id
    dtype: int64
  - name: sentence
    dtype: string
  - name: domain
    dtype: string
  - name: label_0
    dtype: int64
  - name: label_1
    dtype: int64
  - name: label_2
    dtype: int64
  - name: label
    dtype: int64
  splits:
  - name: train
    num_bytes: 1301927
    num_examples: 9800
  - name: validation
    num_bytes: 264729
    num_examples: 2000
  - name: test
    num_bytes: 279521
    num_examples: 2073
  download_size: 739103
  dataset_size: 1846177
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
license: cc-by-nc-sa-4.0
task_categories:
- text-classification
language:
- ro
tags:
- satire
- sentence-level
- news
- nlp
pretty_name: SeLeRoSa processed
size_categories:
- 10K<n<100K
---


# SeLeRoSa - Sentence-Level Romanian Satire Detection Dataset

[![DOI](https://zenodo.org/badge/1003740539.svg)](https://doi.org/10.5281/zenodo.15689793)

## Abstract

> Satire, irony, and sarcasm are techniques that can disseminate untruthful yet plausible information in the news and on social media, akin to fake news. These techniques can be applied at a more granular level, allowing satirical information to be incorporated into news articles. In this paper, we introduce the first sentence-level dataset for Romanian satire detection for news articles, called SeLeRoSa. The dataset comprises 13,873 manually annotated sentences spanning various domains, including social issues, IT, science, and movies. With the rise and recent progress of large language models (LLMs) in the natural language processing literature, LLMs have demonstrated enhanced capabilities to tackle various tasks in zero-shot settings. We evaluate multiple baseline models based on LLMs in both zero-shot and fine-tuning settings, as well as baseline transformer-based models, such as Romanian BERT. Our findings reveal the current limitations of these models in the sentence-level satire detection task, paving the way for new research directions.

## Description

This is the anonymized and processed version of the SeLeRoSa dataset.

The anonymization stage involved using Spacy to replace named entities with tokens:
  * Persons → `<PERSON>`
  * Nationalities/religious/political groups → `<NORP>`
  * Geopolitical entities → `<GPE>`
  * Organizations → `<ORG>`
  * Locations → `<LOC>`
  * Facilities → `<FAC>`

In addition, we replace URLs with the `@URL` tag. Manual filtering of frequently occurring entities not caught by NER was also performed.

The processing step involved:

 * Convert text to lowercase
 * Fix diacritics according to Romanian standards
 * "ţ" and "ş" (cedilla) vs "ț" and "ș" (comma)
 * Tokenize the text using Spacy's tokenizer
 * Remove stop words
 * Remove punctuation marks
 * Remove short tokens (less than 3 characters)
 * Lemmatize text using Spacy's Romanian lemmatizer

For the unprocessed dataset (anonymized only), check https://huggingface.co/datasets/unstpb-nlp/SeLeRoSa
 
The code for processing this dataset, including also the experiments, can be found on [GitHub](https://github.com/razvanalex-phd/SeLeRoSa).

## Usage

First, install the following dependencies:

```bash
pip install datasets torch
```

Example of loading the train set in a dataloader:

```python
from datasets import load_dataset
from torch.utils.data import DataLoader

dataset = load_dataset("unstpb-nlp/SeLeRoSa-proc", split="train")

dataloader = DataLoader(dataset)
for sample in dataloader:
    print(sample)
```

## Dataset structure

The following columns are available for every sample:

| Field | Data Type | Description |
|---|---|---|
| index | int | A unique identifier for every sentence |
| news_id | int | A unique identifier for the source news associated with the current sentence |
| sentence | string | The processed and anonymized sentence |
| domain | string | The domain associated with the sentence. Can be one of: `life-death`, `it-stiinta`, `cronica-de-film` |
| label_0 | int | The label given by the first annotator. 0 - regular, 1 - satirical |
| label_1 | int | The label given by the second annotator 0 - regular, 1 - satirical |
| label_2 | int | The label given by the third annotator 0 - regular, 1 - satirical |
| label | int | The aggregated label through majority voting. This should be used for training and evaluation. 0 - regular, 1 - satirical |


### Citation

If you use this dataset in your research, please cite as follows:

```bibtex
@software{smadu_2025_15689794,
  author       = {Smădu, Răzvan-Alexandru and
                  Iuga, Andreea and
                  Cercel, Dumitru-Clementin and
                  Pop, Florin},
  title        = {SeLeRoSa - Sentence-Level Romanian Satire
                   Detection Dataset
                  },
  month        = jun,
  year         = 2025,
  publisher    = {Zenodo},
  version      = {v1.0.0},
  doi          = {10.5281/zenodo.15689794},
  url          = {https://doi.org/10.5281/zenodo.15689794},
  swhid        = {swh:1:dir:5c0e7a00a415d4346ee7a11f18c814ef3c3f5d88
                   ;origin=https://doi.org/10.5281/zenodo.15689793;vi
                   sit=swh:1:snp:e8bb60f04bd1b01d5e3ac68d7db37a3d28ab
                   7a22;anchor=swh:1:rel:ff1b46be53b410c9696b39aa7f24
                   a3bd387be547;path=razvanalex-phd-SeLeRoSa-83693df
                  },
}
```